@article {pmid40648241,
year = {2025},
author = {Carìa, A},
title = {Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133987},
pmid = {40648241},
issn = {1424-8220},
support = {no number available//5xMille Unitn/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Language ; Artificial Intelligence ; *Communication ; Brain/physiology ; Deep Learning ; Electroencephalography ; Large Language Models ; },
abstract = {Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.},
}

*Brain-Computer Interfaces
Humans
*Language
Artificial Intelligence
*Communication
Brain/physiology
Deep Learning
Electroencephalography
Large Language Models

@article {pmid40648159,
year = {2025},
author = {Ionita, S and Coman, DA},
title = {Narrowband Theta Investigations for Detecting Cognitive Mental Load.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133902},
pmid = {40648159},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Theta Rhythm/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; },
abstract = {The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.},
}

Humans
*Electroencephalography/methods
*Cognition/physiology
*Theta Rhythm/physiology
Algorithms
Signal Processing, Computer-Assisted
Male
Adult
Female

@article {pmid40646750,
year = {2025},
author = {Li, S and Tang, Z and Li, M and Yang, L and Shang, Z},
title = {Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.},
journal = {Animals : an open access journal from MDPI},
volume = {15},
number = {13},
pages = {},
doi = {10.3390/ani15131851},
pmid = {40646750},
issn = {2076-2615},
support = {62301496//the National Natural Science Foundation of China/ ; GZC20232447//the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//the Key Scientific and Technological Projects of Henan Province/ ; 252102210008//the Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.},
}

@article {pmid40645213,
year = {2025},
author = {Meng, L and Zhao, H and Dong, M and Wang, Q and Shi, Y and Wang, D and Zhu, X and Xu, R and Ming, D},
title = {Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeeca},
pmid = {40645213},
issn = {1741-2552},
abstract = {OBJECTIVE: The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults.

APPROACH: We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched older adults (OA) and 15 young adults (YA) completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.

MAIN RESULTS: Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.

SIGNIFICANCE: The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.},
}

@article {pmid40645212,
year = {2025},
author = {Li, Y and Zhao, Z and Liu, J and Peng, Y and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeec8},
pmid = {40645212},
issn = {1741-2552},
abstract = {OBJECT: Speech imagery is a nascent paradigm that is receiving widespread attention in current Brain-Computer Interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.

APPROACH: In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e., projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e., dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.

MAIN RESULTS: To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.

SIGNIFICANCE: Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.},
}

@article {pmid40644990,
year = {2025},
author = {Cai, G and Chen, Y and Yang, B and Yang, Y and Ma, T and Wang, Y},
title = {CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107795},
doi = {10.1016/j.neunet.2025.107795},
pmid = {40644990},
issn = {1879-2782},
abstract = {The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.},
}

@article {pmid40644885,
year = {2025},
author = {Al-Hadithy, SS and Abdalkafor, AS and Al-Khateeb, B},
title = {Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110713},
doi = {10.1016/j.compbiomed.2025.110713},
pmid = {40644885},
issn = {1879-0534},
abstract = {A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.},
}

@article {pmid40644284,
year = {2025},
author = {Petrich, LC and Neumann, S and Pilarski, PM and Fyshe, A},
title = {Neural Network Sparsity in Brain-Body-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1-8},
doi = {10.1109/ICORR66766.2025.11062950},
pmid = {40644284},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.},
}

*Brain-Computer Interfaces
Humans
Electroencephalography/methods
*Neural Networks, Computer
Algorithms
*Signal Processing, Computer-Assisted

@article {pmid40644274,
year = {2025},
author = {Patarini, F and Maronati, C and Manuello, J and Cuturi, LF and Monti, M and Savina, G and Ferrari, E and Iarrobino, I and Iani, C and Rubichi, S and Ciaramidaro, A and Astolfi, L and Cavallo, A and Toppi, J},
title = {Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1078-1082},
doi = {10.1109/ICORR66766.2025.11062958},
pmid = {40644274},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Electroencephalography ; *Stroke Rehabilitation ; Male ; Female ; Adult ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.},
}

Humans
*Brain-Computer Interfaces
Biomechanical Phenomena/physiology
Electroencephalography
*Stroke Rehabilitation
Male
Female
Adult
Middle Aged

@article {pmid40644240,
year = {2025},
author = {Gonzalez-Cely, AX and Soekadar, SR and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {76-81},
doi = {10.1109/ICORR66766.2025.11063181},
pmid = {40644240},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Male ; Adult ; Electroencephalography ; *Exercise Test ; *Imagination/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.},
}

Humans
*Brain-Computer Interfaces
*Lower Extremity/physiology
Male
Adult
Electroencephalography
*Exercise Test
*Imagination/physiology
Female
Signal Processing, Computer-Assisted

@article {pmid40644193,
year = {2025},
author = {Mannan, MMN and Lloyd, DG and Pizzolato, C},
title = {Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {284-288},
doi = {10.1109/ICORR66766.2025.11063010},
pmid = {40644193},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.},
}

Humans
*Brain-Computer Interfaces
Electroencephalography
Male
Adult
Female
Signal Processing, Computer-Assisted
Young Adult

@article {pmid40644184,
year = {2025},
author = {Koellner, J and Wimpff, M and Gizzi, L and Vujaklija, I and Yang, B},
title = {Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {546-552},
doi = {10.1109/ICORR66766.2025.11063023},
pmid = {40644184},
issn = {1945-7901},
mesh = {Humans ; *Deep Learning ; Magnetoencephalography ; Male ; Adult ; Female ; Brain-Computer Interfaces ; *Cerebral Cortex/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Resistance Training ; },
abstract = {Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.},
}

Humans
*Deep Learning
Magnetoencephalography
Male
Adult
Female
Brain-Computer Interfaces
*Cerebral Cortex/physiology
Young Adult
Signal Processing, Computer-Assisted
Resistance Training

@article {pmid40644160,
year = {2025},
author = {Toppi, J and Savina, G and Colamarino, E and De Seta, V and Patarini, F and Cincotti, F and Pichiorri, F and Mattia, D},
title = {Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {431-436},
doi = {10.1109/ICORR66766.2025.11062988},
pmid = {40644160},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; Aged ; Movement/physiology ; Adult ; Stroke/physiopathology ; Electromyography ; },
abstract = {Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.},
}

Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Male
Middle Aged
Female
Aged
Movement/physiology
Adult
Stroke/physiopathology
Electromyography

@article {pmid40644144,
year = {2025},
author = {Bastos-Filho, T and Gonzalez-Cely, AX and Mehrpour, S and Souza, F and Villa-Parra, AC and Cabral, F},
title = {Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1203-1208},
doi = {10.1109/ICORR66766.2025.11063073},
pmid = {40644144},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Neurofeedback/methods ; Male ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Chronic Disease ; Electromyography ; Middle Aged ; Stroke/physiopathology ; },
abstract = {This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.},
}

Humans
*Stroke Rehabilitation/methods
*Neurofeedback/methods
Male
Brain-Computer Interfaces
*Electric Stimulation Therapy/methods
Chronic Disease
Electromyography
Middle Aged
Stroke/physiopathology

@article {pmid40644135,
year = {2025},
author = {Sun, Q and Merino, EC and Yang, L and Faes, A and Van Hulle, MM},
title = {On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {186-192},
doi = {10.1109/ICORR66766.2025.11063039},
pmid = {40644135},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Proprioception/physiology ; Male ; *Exoskeleton Device ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Hand/physiology ; Movement/physiology ; Fingers/physiology ; },
abstract = {Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.},
}

Humans
*Electroencephalography/methods
*Proprioception/physiology
Male
*Exoskeleton Device
Female
Adult
Brain-Computer Interfaces
Young Adult
*Hand/physiology
Movement/physiology
Fingers/physiology

@article {pmid40644105,
year = {2025},
author = {Shevchenko, O and Yeremeieva, S and Laschowski, B},
title = {Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {222-227},
doi = {10.1109/ICORR66766.2025.11063033},
pmid = {40644105},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.},
}

*Brain-Computer Interfaces
Humans
*Algorithms
Electroencephalography/methods
*Signal Processing, Computer-Assisted
Brain/physiology
Machine Learning
Neural Networks, Computer

@article {pmid40644100,
year = {2025},
author = {Feng, Z and Kakkos, I and Matsopoulos, GK and Guan, C and Sun, Y},
title = {Explaining E/MEG Source Imaging and Beyond: An Updated Review.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3588350},
pmid = {40644100},
issn = {2168-2208},
abstract = {E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.},
}

@article {pmid40644042,
year = {2025},
author = {Kim, M and Jo, S and Cho, H and Ye, S and Kim, Y and Park, HS},
title = {Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1564-1569},
doi = {10.1109/ICORR66766.2025.11063079},
pmid = {40644042},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Electromyography/methods/instrumentation ; *Wrist/physiology ; Male ; Adult ; Wearable Electronic Devices ; Stroke Rehabilitation ; *Robotics/instrumentation ; Female ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.},
}

Humans
*Electroencephalography/methods
*Hand/physiology
*Electromyography/methods/instrumentation
*Wrist/physiology
Male
Adult
Wearable Electronic Devices
Stroke Rehabilitation
*Robotics/instrumentation
Female
Signal Processing, Computer-Assisted
Brain-Computer Interfaces

@article {pmid40640801,
year = {2025},
author = {Gao, W and Yan, Z and Zhou, H and Xie, Y and Wang, H and Yang, J and Yu, J and Ni, C and Liu, P and Xie, M and Huang, L and Ye, Z},
title = {Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {498},
pmid = {40640801},
issn = {1477-3155},
support = {National Innovation Platform Development Program (No. 2020021105012440), the National Natural Science Foundation of China (No. 82172524, 81974355)//Zhewei Ye/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electrodes, Implanted ; Animals ; *Biocompatible Materials/chemistry ; Brain/physiology ; },
abstract = {Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.},
}

*Brain-Computer Interfaces
Humans
*Electrodes, Implanted
Animals
*Biocompatible Materials/chemistry
Brain/physiology

@article {pmid40640486,
year = {2025},
author = {Pierrieau, E and Dussard, C and Plantey-Veux, A and Guerrini, C and Lau, B and Pillette, L and George, N and Jeunet-Kelway, C},
title = {Changes in cortical beta power predict motor control flexibility, not vigor.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1041},
pmid = {40640486},
issn = {2399-3642},
support = {ANR-20-CE37-0012//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {Humans ; Male ; Female ; *Beta Rhythm/physiology ; Adult ; Electroencephalography ; Young Adult ; *Motor Cortex/physiology ; Neurofeedback ; Brain-Computer Interfaces ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.},
}

Humans
Male
Female
*Beta Rhythm/physiology
Adult
Electroencephalography
Young Adult
*Motor Cortex/physiology
Neurofeedback
Brain-Computer Interfaces
Movement/physiology
Psychomotor Performance/physiology

@article {pmid40638250,
year = {2025},
author = {Zhang, X and Ma, D and Wang, J and Su, N and Guo, J},
title = {Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.},
journal = {Physiology (Bethesda, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1152/physiol.00011.2025},
pmid = {40638250},
issn = {1548-9221},
support = {2020YFA0908501//Ministry of Science and Technology of China/ ; 32371204//National Science Foundation of China/ ; LD25C050004//Zhejiang Provincial Natural Science Foundation/ ; //Foundamental Research Funds for the Central Universities/ ; //Ministry of Education Frontier Science Center for Brain Science & Brain-Machine Integration/ ; //K.C. Wong Education Foundation/ ; },
abstract = {Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.},
}

@article {pmid40636103,
year = {2025},
author = {Qi, R and Lin, Y and Liu, S and Cao, X and Xie, M and Yu, C and Sun, H and Gao, L and Li, X},
title = {Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.},
journal = {National science review},
volume = {12},
number = {7},
pages = {nwaf162},
pmid = {40636103},
issn = {2053-714X},
abstract = {Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.},
}

@article {pmid40634126,
year = {2025},
author = {Abbagnano, E and Pascual-Valdunciel, A and Zicher, B and Ibáñez, J and Farina, D},
title = {Projection of Cortical Beta Band Oscillations to a Motor Neuron Pool Across the Full Range of Recruitment.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0453-25.2025},
pmid = {40634126},
issn = {1529-2401},
abstract = {Cortical beta band oscillations (13-30 Hz) are associated with sensorimotor control, but their precise role remains unclear. Evidence suggests that for low-threshold motor neurons, these oscillations are conveyed to muscles via the fastest corticospinal fibers. However, their transmission to motor neurons of different sizes may vary due to differences in the relative strength of corticospinal and reticulospinal projections across the motor neuron pool. Consequently, it remains uncertain whether corticospinal beta transmission follows similar pathways and maintains consistent strength across the entire motor neuron pool. To investigate this, we examined beta activity in motor neurons innervating the tibialis anterior muscle across the full range of recruitment thresholds in a study involving 12 participants of both sexes. We characterized beta activity at both the cortical and motor unit levels while participants performed contractions from mild to submaximal levels. Corticomuscular coherence remained unchanged across contraction forces after normalizing for the net motor unit spike rate, suggesting that beta oscillations are transmitted with similar strength to motor neurons, regardless of size. To further explore beta transmission, we estimated corticospinal delays using the cumulant density function, identifying peak correlations between cortical and muscular activity. Once compensated for variable peripheral axonal propagation delay across motor neurons, the corticospinal delay remained stable, and its value (approximately 14 ms) indicated projections through the fastest corticospinal fibers for all motor neurons. These findings demonstrate that corticospinal beta band transmission is determined by the fastest pathway connecting in the corticospinal tract, projecting similarly across the entire motor neuron pool.Significance Statement Beta band oscillations (13-30 Hz) play a key role in sensorimotor control, yet their precise transmission to motor neurons remains unclear. This study demonstrates that beta oscillations are transmitted similarly across the entire motor neuron pool, regardless of recruitment threshold. By examining corticomuscular coherence and corticospinal delays during voluntary contractions, we show that beta activity is consistently relayed to motor neurons via the fastest corticospinal fibers. These findings provide evidence that beta band activity is not preferentially directed toward specific subsets of motor neurons but is instead a global signal influencing motor output. This insight advances our understanding of how the central nervous system regulates movement and may have implications for neurorehabilitation and brain-machine interfaces.},
}

@article {pmid40632037,
year = {2025},
author = {Kumar, R and Soni, A and Ahmed, T and Beniwal, K},
title = {Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.},
journal = {Journal of trauma nursing : the official journal of the Society of Trauma Nurses},
volume = {32},
number = {4},
pages = {189-200},
pmid = {40632037},
issn = {1078-7496},
mesh = {Humans ; India ; Adult ; Female ; Male ; *Quality of Life/psychology ; Surveys and Questionnaires ; *Trauma Nursing ; Job Satisfaction ; Resilience, Psychological ; *Nursing Staff, Hospital/psychology ; Trauma Centers ; Burnout, Professional/psychology ; },
abstract = {BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.

OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.

METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.

RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7 years (standard deviation [SD] = 2.89) and average years of trauma experience of 2.08 years (SD = 1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r = -.23, p = .037). Burnout positively correlated with anxiety (r = .24, p = .033) and sleep disturbances (r = .34, p = .023), and negatively with nurses' resilience (r = -.12, p = .049). Professional quality of life significantly correlated with resilience (r = .18, p = .048). Resilience mediated the relationship between anxiety and both burnout (β = .24, bootstrap confidence interval [BCI] = 0.04, 0.46, p = .041) and secondary traumatic stress (β = .24, BCI = 0.03, 0.52, p = .029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.

CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.},
}

Humans
India
Adult
Female
Male
*Quality of Life/psychology
Surveys and Questionnaires
*Trauma Nursing
Job Satisfaction
Resilience, Psychological
*Nursing Staff, Hospital/psychology
Trauma Centers
Burnout, Professional/psychology

@article {pmid40631920,
year = {2025},
author = {Wang, X and Jun, F and Lin, C and Wang, X},
title = {Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00418},
pmid = {40631920},
issn = {1948-7193},
abstract = {Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.},
}

@article {pmid40631106,
year = {2025},
author = {Ding, Y and Dunn, SLS and Sakon, JJ and Duan, C and Zhang, Y and Berger, JI and Rhone, AE and Nourski, KV and Kawasaki, H and Howard, MA and Roychowdhury, VP and Fried, I},
title = {Reading specific memories from human neurons before and after sleep.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662486},
pmid = {40631106},
issn = {2692-8205},
abstract = {The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory [1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model [2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention [4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles [5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.},
}

@article {pmid40631097,
year = {2025},
author = {Lee, W and Scherschligt, X and Nishimoto, M and Rouse, AG},
title = {Neural trajectories improve motor precision.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662682},
pmid = {40631097},
issn = {2692-8205},
abstract = {Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.},
}

@article {pmid40505654,
year = {2025},
author = {Becker, B},
title = {Will our social brain inherently shape and be shaped by interactions with AI?.},
journal = {Neuron},
volume = {113},
number = {13},
pages = {2037-2041},
doi = {10.1016/j.neuron.2025.04.034},
pmid = {40505654},
issn = {1097-4199},
mesh = {Humans ; *Brain/physiology ; *Artificial Intelligence ; *Social Behavior ; *Interpersonal Relations ; *Social Interaction ; Animals ; *Brain-Computer Interfaces ; },
abstract = {Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.},
}

Humans
*Brain/physiology
*Artificial Intelligence
*Social Behavior
*Interpersonal Relations
*Social Interaction
Animals
*Brain-Computer Interfaces

@article {pmid40630938,
year = {2025},
author = {Liu, L and Wang, F and Chen, X and Liu, L and Wang, Y and Bei, J and Lei, L and Zhao, Z and Tang, C},
title = {Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.},
journal = {International journal of nanomedicine},
volume = {20},
number = {},
pages = {8693-8728},
pmid = {40630938},
issn = {1178-2013},
mesh = {*Needles ; Humans ; *Drug Delivery Systems/instrumentation/methods ; Tissue Engineering/methods ; *Biomedical Engineering/methods/instrumentation ; Animals ; Biocompatible Materials/chemistry ; Equipment Design ; *Microinjections/instrumentation ; Brain-Computer Interfaces ; },
abstract = {This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.},
}

*Needles
Humans
*Drug Delivery Systems/instrumentation/methods
Tissue Engineering/methods
*Biomedical Engineering/methods/instrumentation
Animals
Biocompatible Materials/chemistry
Equipment Design
*Microinjections/instrumentation
Brain-Computer Interfaces

@article {pmid40630584,
year = {2025},
author = {Hahn, NV and Stein, E and , and Donoghue, JP and Simeral, JD and Hochberg, LR and Willett, FR},
title = {Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.02.25330310},
pmid = {40630584},
abstract = {Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.},
}

@article {pmid40629288,
year = {2025},
author = {Paret, C and Jindrová, M and Kleindienst, N and Eck, J and Breman, H and Lührs, M and Barth, B and Ethofer, T and Fallgatter, AJ and Goebel, R and Hoell, A and Lockhofen, D and Reinhold, AS and Maier, S and Matthies, S and Mulert, C and Schönholz, C and van Elst, LT and Schmahl, C},
title = {A randomised controlled trial of amygdala fMRI-neurofeedback versus sham-feedback in borderline-personality disorder - systematic literature review and introduction to the BrainSTEADy trial.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {687},
pmid = {40629288},
issn = {1471-244X},
support = {PA 3107/4-1//Deutsche Forschungsgemeinschaft/ ; SCHM 1526/26-1//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Adult ; Female ; Humans ; Male ; *Amygdala/physiopathology/diagnostic imaging ; *Borderline Personality Disorder/therapy/physiopathology/diagnostic imaging ; Emotional Regulation ; *Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; },
abstract = {BACKGROUND: Individuals with Borderline-Personality Disorder (BPD) experience intensive, unstable negative emotions. Hyperactivity of the amygdala is assumed to drive exaggerated emotional responses in BPD. Functional Magnetic Resonance Imaging (fMRI)-based neurofeedback is an endogenous neuromodulation method intended to address the imbalance of neural circuits and thus holds the potential as a treatment for BPD. Many original articles and meta-analyses show that fMRI-neurofeedback can improve psychiatric symptoms. In contrast, there is a lack of publications that aggregate and evaluate data of the safety of the treatment. Furthermore, evidence on the efficacy of fMRI-neurofeedback for the treatment of BPD is limited. Preliminary evidence suggests that downregulation of amygdala hyperactivation through fMRI-neurofeedback can ameliorate emotion dysregulation. To test this assumption, BrainSTEADy (Brain Signal Training to Enhance Affect Down-regulation), a multi-center clinical trial, is conducted. First, we present a systematic literature review evaluating the safety of fMRI-neurofeedback and assessing clinical performance in BPD. Second, we describe the study protocol of BrainSTEADy.

METHODS: Literature research: From 2,609 screened paper abstracts, 758 were identified as potentially relevant. Twenty studies reported adverse events or undesirable side effects. Two papers provided relevant data for the assessment of clinical performance in BPD. BrainSTEADy study protocol: During four sessions, patients will receive graphical fMRI-neurofeedback from their right amygdala or sham-feedback while viewing images with aversive content. The primary endpoint, 'negative affect intensity', will be assessed after the last neurofeedback session using Ecological Momentary Assessment (EMA). Secondary endpoints will be assessed after the last neurofeedback session, at 3-month and at 6-month follow-up. This trial is a multi-center, patient- and investigator-blind, randomized, parallel-group superiority study with a planned interim-analysis once half of the recruitment target is met (N = 82).

DISCUSSION: As suggested by literature review, fMRI-neurofeedback is a safe treatment for patients, although future studies should systematically assess and report adverse events. Although fMRI-neurofeedback showed promising effects in BPD, current evidence is limited and calls for a randomized controlled trial such as BrainSTEADy, which aims to test whether amygdala-fMRI-neurofeedback specifically reduces emotion instability in BPD beyond nonspecific benefit. Endpoint measures encompassing EMA, clinical interviews, psychological questionnaires, quality of life, and neuroimaging will enable a comprehensive analysis of effects and mechanisms of neurofeedback treatment.

TRIAL REGISTRATION: The study protocol was first posted 2024/10/04 on ClinicalTrials.gov and received the ID NCT06626789.},
}

Adult
Female
Humans
Male
*Amygdala/physiopathology/diagnostic imaging
*Borderline Personality Disorder/therapy/physiopathology/diagnostic imaging
Emotional Regulation
*Magnetic Resonance Imaging/methods
*Neurofeedback/methods
Randomized Controlled Trials as Topic
Multicenter Studies as Topic

@article {pmid40629037,
year = {2025},
author = {Wood, H},
title = {Brain-computer interface restores naturalistic speech to a man with ALS.},
journal = {Nature reviews. Neurology},
volume = {},
number = {},
pages = {},
pmid = {40629037},
issn = {1759-4766},
}

@article {pmid40628758,
year = {2025},
author = {Mathiyazhagan, S and Devasena, MSG},
title = {Motor imagery EEG signal classification using novel deep learning algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24539},
pmid = {40628758},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Wavelet Analysis ; *Imagination/physiology ; },
abstract = {Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. To begin, the preprocessing stage of the proposed approach includes an innovative hybrid approach that combines empirical mode decomposition (EMD) for extracting intrinsic signal modes. In addition to that, continuous wavelet transform (CWT) is used for multi-resolution analysis. For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. To attain enhanced performance the parameters of the classifier network are optimized using the Far and near optimization (FNO) algorithm. This combined approach provides superior classification accuracy and adaptability to diverse conditions in EEG signal analysis. The evaluations of the proposed approach were conducted using benchmark BCI competition IV Dataset 2a and Physionet dataset. On the BCI dataset, the proposed approach achieves 95.7% accuracy, 96.2% recall, 95.9% precision, and 97.5% specificity. In addition, it delivers 94.1% accuracy, 94.0% recall, 93.6% precision, and 95.0% specificity on the PhysioNet dataset. With better results, the proposed model attained superior performance compared to existing methods such as CNN, LSTM, and BiLSTM algorithms.},
}

*Electroencephalography/methods
Humans
*Deep Learning
Algorithms
*Signal Processing, Computer-Assisted
Brain-Computer Interfaces
Wavelet Analysis
*Imagination/physiology

@article {pmid40628277,
year = {2025},
author = {Afdideh, F and Shamsollahi, MB},
title = {Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/aded19},
pmid = {40628277},
issn = {2057-1976},
abstract = {Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 5.11% for MI and 97.72 4.55% for Motor Execution (ME) after just a single training session. .},
}

@article {pmid40628276,
year = {2025},
author = {Fedosov, N and Medvedeva, D and Shevtsov, O and Ossadtchi, A},
title = {A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aded35},
pmid = {40628276},
issn = {1741-2552},
abstract = {\textbf{Objective.}
Recent advances in biomagnetic sensing have led to the development of compact, wearable devices capable of detecting weak magnetic fields generated by biological activity. Optically pumped magnetometers (OPMs) have shown significant promise in functional neuroimaging. Brain rhythms play a crucial role in diagnostics, cognitive research, and neurointerfaces. Here we demonstrate that a small number of OPMs can reliably capture sensorimotor rhythms (SMR). \textbf{Approach.}
We conducted real-movement and motor-imagery experiments with nine participants in two distinct magnetically shielded rooms (MSR), each equipped with different ambient field suppression systems. We used only 3 OPMs located above the sensorimotor region and standard common-spatial-patterns (CSP) based processing to decode the real and imaginary movement intentions of our participants. We evaluated reproducibility of the CSP components' spectral profiles and assessed the decoding accuracy deterioration with reduction of OPM's count. We also assessed the influence of the magnetic field orientation on the decoding accuracy and implemented a real-time motor imagery BCI solution. \textbf{Main Results.}
Under optimal conditions, OPM sensors deliver informative signals suitable for practical motor imagery brain-computer interface (BCI) applications. Those subjects who participated in the experiments in both MSRs exhibit highly reproducible SMR spectral patterns across two different magnetically shielded environments. The magnetic field components with radial orientation yield higher decoding accuracy than their tangential counterparts. In some subjects we observed more than 80 \% of binary decoding accuracy using a single OPM sensor. Finally we demonstrate real-time performance of our system along with clearly pronounced and behaviorally relevant fluctuations of the SMR power. \textbf{Significance.}
For the first time, we demonstrated reliable and reproducible tracking of sensorimotor rhythm components using a small number of contactless OPM sensors during real movements and motor imagery. Our findings pave the way for more efficient post-stroke neurorehabilitation by enabling motor imagery-based BCI solutions to accelerate functional recovery.},
}

@article {pmid40627787,
year = {2025},
author = {Li, Y and Zhang, J},
title = {Utilizing statistical analysis for motion imagination classification in brain-computer interface systems.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0327121},
doi = {10.1371/journal.pone.0327121},
pmid = {40627787},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; *Motion ; *Brain/physiology ; Male ; },
abstract = {In this study, we introduce a novel Field-Agnostic Riemannian-Kernel Alignment (FARKA) method to advance the classification of motion imagination in Brain-Computer Interface (BCI) systems. BCI systems enable direct control of external devices through brain activity, bypassing peripheral nerves and muscles. Among various BCI technologies, electroencephalography (EEG) based on non-intrusive cortical potential signals stands out due to its high temporal resolution and non-invasive nature. EEG-based BCI technology encodes human brain intentions into cortical potentials, which are recorded and decoded into control commands. This technology is crucial for applications in motion rehabilitation, training optimization, and motion control. The proposed FARKA method combines Riemannian Alignment for sample alignment, Riemannian Tangent Space for spatial representation extraction, and Knowledge Kernel Adaptation to learn field-agnostic kernel matrices. Our approach addresses the limitations of current methods by enhancing classification performance and efficiency in inter-individual MI tasks. Experimental results on three public EEG datasets demonstrate the superior performance of FARKA compared to existing methods.},
}

*Brain-Computer Interfaces
Humans
Electroencephalography/methods
*Imagination/physiology
Algorithms
*Motion
*Brain/physiology
Male

@article {pmid40627473,
year = {2025},
author = {Zhao, Z and Cao, Y and Yu, H and Yu, H and Huang, J},
title = {CNNViT-MILF-a: A Novel Architecture Leveraging the Synergy of CNN and ViT for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3587026},
pmid = {40627473},
issn = {2168-2208},
abstract = {Accurate motor imagery (MI) classification in EEG-based brain-computer interfaces (BCIs) is essential for applications in engineering, medicine, and artificial intelligence. Due to the limitations of single-model approaches, hybrid model architectures have emerged as a promising direction. In particular, convolutional neural networks (CNNs) and vision transformers (ViTs) demonstrate strong complementary capabilities, leading to enhanced performance. This study proposes a series of novel models, termed as CNNViT-MI, to explore the synergy of CNNs and ViTs for MI classification. Specifically, five fusion strategies were defined: parallel integration, sequential integration, hierarchical integration, early fusion, and late fusion. Based on these strategies, eight candidate models were developed. Experiments were conducted on four datasets: BCI competition IV dataset 2a, BCI competition IV dataset 2b, high gamma dataset, and a self-collected MI-GS dataset. The results demonstrate that CNNViT-MILF-a achieves the best performance among all candidates by leveraging ViT as the backbone for global feature extraction and incorporating CNN-based local representations through a late fusion strategy. Compared to the best-performing state-ofthe-art (SOTA) methods, mean accuracy was improved by 2.27%, 2.31%, 0.74%, and 2.50% on the respective datasets, confirming the model's effectiveness and broad applicability, other metrics showed similar improvements. In addition, significance analysis, ablation studies, and visualization analysis were conducted, and corresponding clinical integration and rehabilitation protocols were developed to support practical use in healthcare.},
}

@article {pmid40627471,
year = {2025},
author = {Chen, X and Fu, Z and Zhang, P and Chen, X and Huang, J},
title = {Intracortical Brain-Machine Interfaces with High-Performance Neural Decoding through Efficient Transfer Meta-learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3586870},
pmid = {40627471},
issn = {1558-2531},
abstract = {Implantable brain-machine interfaces (iBMIs) have emerged as a groundbreaking neural technology for restoring motor function and enabling direct neural communication pathways. Despite their therapeutic potential in neurological rehabilitation, the critical challenge of neural decoder calibration persists, particularly in the context of transfer learning. Traditional calibration approaches assume the availability of extensive neural recordings, which is often impractical in clinical settings due to patient fatigue and neural signal variability. Furthermore, the inherent constraints of implanted neural processors-including limited computational capacity and power consumption requirements-demand streamlined processing algorithms. To address these clinical and technical challenges, we developed DMM-WcycleGAN (Dimensionality Reduction Model-Agnostic Meta-Learning based Wasserstein Cycle Generative Adversarial Networks), a novel neural decoding framework that integrates meta-learning principles with optimal transfer learning strategies. This innovative approach enables efficient decoder calibration using minimal neural data while implementing dimensionality reduction techniques to optimize computational efficiency in implanted devices. In vivo experiments with non-human primates demonstrated DMM-WcycleGAN's superior performance in mitigating neural signal distribution shifts between historical and current recordings, achieving a 3% enhancement in neural decoding accuracy using only ten calibration trials while reducing the calibration duration by over 70%, thus significantly improving the clinical viability of iBMI systems.},
}

@article {pmid40626564,
year = {2025},
author = {Hu, Z and Luo, K and Liu, Y},
title = {Classification of motor imagery based on multi-scale feature extraction and fusion-residual temporal convolutional network.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2528892},
pmid = {40626564},
issn = {1476-8259},
abstract = {Brain-computer interface (BIC) decodes electroencephalogram (EEG) signals to realize the interaction between brain and external devices. However, traditional methods show limited performance in motor imagery electroencephalogram (MI-EEG) classification. In this paper, we introduce a multi-scale temporal convolutional network (MS-TCNet) that employs parallel multi-scale convolutions for spatiotemporal feature extraction, efficient channel attention (ECA) for channel weights optimization, and fusion-residual temporal convolution (FR-TCN) for high-level temporal feature capture. Experimental results show that MS-TCNet achieved remarkable decoding accuracies of 87.85% and 92.85% on the BCI IV-2a and BCI IV-2b datasets, respectively. The proposed MS-TCNet surpasses existing baseline models across various performance metrics, demonstrating its effectiveness in advancing MI-EEG decoding.},
}

@article {pmid40624803,
year = {2025},
author = {Li, S and Gao, S and Hu, Y and Xu, J and Sheng, W},
title = {Brain-Computer Interfaces in Spinal Cord Injury: A Promising Therapeutic Strategy.},
journal = {The European journal of neuroscience},
volume = {62},
number = {1},
pages = {e70183},
doi = {10.1111/ejn.70183},
pmid = {40624803},
issn = {1460-9568},
support = {2023TSYCLJ0031//Program of Technological Leading Talent of Tianshan Talent/ ; 2023YFY-QKMS-06//Youth Foundation of Research and Development/ ; 2021D01D18//Key Program of Natural Science Foundation of Xinjiang Uygur Autonomous Region/ ; 82360257//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Neurological Rehabilitation/methods ; Animals ; },
abstract = {The current treatment regimen for spinal cord injury (SCI), a neurological disorder with a high incidence of disability, is based on early surgical decompression and administration of pharmacological agents. However, the efficacy of such an approach remains limited, and most patients have sensory and functional deficits below the level of injury, which seriously affects their quality of life. This necessitates further exploration into effective treatment modalities. In recent years, considerable advancements have been made in developing and utilizing brain-computer interfaces (BCI), which facilitate neurorehabilitation and enhance motor function by transforming brain signals into diverse forms of output commands. BCI-assisted systems provide alternative means of rehabilitative exercise or limb movement in patients with SCI, including electrical stimulation and exoskeleton robots. BCI shows great potential in the rehabilitation of patients with SCI. This review summarizes the current research status and limitations of BCI for SCI to provide novel insights into the concept of multimodal rehabilitation and treatment of SCI and facilitate BCI's future development.},
}

Humans
*Brain-Computer Interfaces
*Spinal Cord Injuries/rehabilitation/physiopathology/therapy
*Neurological Rehabilitation/methods
Animals

@article {pmid40624755,
year = {2025},
author = {Barios, JA and Vales, Y and Catalán, JM and Blanco-Ivorra, A and Martínez-Pascual, D and García-Aracil, N},
title = {Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.},
journal = {International journal of neural systems},
volume = {35},
number = {9},
pages = {2550044},
doi = {10.1142/S0129065725500443},
pmid = {40624755},
issn = {1793-6462},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Middle Aged ; Female ; Aged ; *Stroke/physiopathology/diagnosis ; *Exoskeleton Device ; *Sensorimotor Cortex/physiopathology ; *Beta Rhythm/physiology ; Electroencephalography ; Adult ; Longitudinal Studies ; *Motor Activity/physiology ; Brain-Computer Interfaces ; Movement/physiology ; },
abstract = {Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.},
}

Humans
*Stroke Rehabilitation/methods/instrumentation
Male
Middle Aged
Female
Aged
*Stroke/physiopathology/diagnosis
*Exoskeleton Device
*Sensorimotor Cortex/physiopathology
*Beta Rhythm/physiology
Electroencephalography
Adult
Longitudinal Studies
*Motor Activity/physiology
Brain-Computer Interfaces
Movement/physiology

@article {pmid40622874,
year = {2025},
author = {Annett, EG and Shook, JR and Giordano, J},
title = {Super Soldiers or Social Burden? Ethical Exploration of the Benefits and Costs of Military Bioenhancement.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/21507740.2025.2519457},
pmid = {40622874},
issn = {2150-7759},
abstract = {Biotechnological enhancements for military personnel arouse scrutiny, beyond the ethics of experimental research and due care during operational service, to the eventual return to a civilian life. Reversal of enhancements-by withdrawal, extraction, deactivation, modification, destruction, etc.-will be just as experimental and consequential. Super soldiering may not smoothly transition to ordinary habilitation and lifestyle. Complete reversions of dramatic augmentations, such as prosthetics or brain-computer interfacing, could be more damaging to the person than the initial installation. Partial reversions would be just as perplexing, as discharged personnel retain workable technology to prevent disability while other careers next beckon for a suitably empowered individual. Either way, all such biotechnological enhancements must be treated as ethical and social experiments having both positive and negative potential outcomes. Life stages of technologically modified military personnel require special ethical consideration beyond the lifecycle of the technology itself. The post-enhancement veteran is a largely unexplored area, and we propose that these civilian "supra-soldiers" will become a cohort of increasing interest, requiring continued care and ethical support. To that end, we suggest a system of guidelines to ensure ethically sound support for those who serve, and have served, in national defense.},
}

@article {pmid40622660,
year = {2025},
author = {Kong, L and Zhu, B and Zhuang, Y and Lai, J and Hu, S},
title = {Viewing Psychiatric Disorders Through Viruses: Simple Architecture, Burgeoning Implications.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40622660},
issn = {1995-8218},
abstract = {A growing interest in the comprehensive pathogenic mechanisms of psychiatric disorders from the perspective of the microbiome has been witnessed in recent decades; the intrinsic link between microbiota and brain function through the microbiota-gut-brain axis or other pathways has gradually been realized. However, little research has focused on viruses-entities characterized by smaller dimensions, simpler structures, greater diversity, and more intricate interactions with their surrounding milieu compared to bacteria. To date, alterations in several populations of bacteriophages and viruses have been documented in both mouse models and patients with psychiatric disorders, including schizophrenia, major depressive disorder, autism spectrum disorder, and Alzheimer's disease, accompanied by metabolic disruptions that may directly or indirectly impact brain function. In addition, eukaryotic virus infection-mediated brain dysfunction provides insights into the psychiatric pathology involving viruses. Efforts towards virus-based diagnostic and therapeutic approaches have primarily been documented. However, limitations due to the lack of large-scale cohort studies, reliability, clinical applicability, and the unclear role of viruses in microbiota interactions pose a challenge for future studies. Nevertheless, it is conceivable that investigations into viruses herald a new era in the field of precise psychiatry.},
}

@article {pmid40621214,
year = {2025},
author = {Kwon, J and Min, BK},
title = {Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545726},
pmid = {40621214},
issn = {1662-5161},
abstract = {We investigated whether the phase-lag types of cross-frequency coupled alternating current stimulation (CFC-tACS), a non-invasive technique aimed at enhancing cognitive functions, could be decoded using task-based electroencephalographic (EEG) signals. EEG recordings were obtained from 21 healthy individuals engaged in a modified Sternberg task. CFC-tACS was administered online for 6 s during the middle of the retention period with either a 45° or 180° phase lag between the central executive network and the default mode network. To decode different phase-lag tACS conditions, we trained a modified EEGNet using task-based EEG signals before and after the online tACS application. When utilizing parietal EEG signals, the model achieved a decoding accuracy of 81.73%. Feature maps predominantly displayed EEG beta activity in the parietal region, suggesting that the model heavily weighted the beta band, indicative of top-down cognitive control influenced by tACS phase-lag type. Thus, EEG signals can decode online stimulation types, and task-related EEG spectral characteristics may indicate neuromodulatory activity during brain stimulation. This study could advance communicative strategies in brain-machine interfacing (BMI)-neuromodulation within a closed-loop system.},
}

@article {pmid40620352,
year = {2025},
author = {Ying, A and Lv, J and Huang, J and Wang, T and Si, P and Zhang, J and Zuo, G and Xu, J},
title = {A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1591398},
pmid = {40620352},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI)-based brain-computer interfaces (BCI) offers promising applications in rehabilitation. Traditional force-based MI-BCI paradigms generally require subjects to imagine constant force during static or dynamic state. It is challenging to meet the demands of dynamic interaction with force intensity variation in MI-BCI systems.

METHODS: To address this gap, we designed a novel MI paradigm inspired by daily life, where subjects imagined variations in force intensity during dynamic unilateral upper-limb movements. In a single trial, the subjects were required to complete one of three combinations of force intensity variations: large-to-small, large-to-medium, or medium-to-small. During the execution of this paradigm, electroencephalography (EEG) features exhibit dynamic coupling, with subtle variations in intensity, timing, frequency coverage, and spatial distribution, as the force intensity imagined by the subjects changed. To recognize these fine-grained features, we propose a feature fusion network with a spatial-temporal-enhanced strategy and an information reconstruction (FN-SSIR) algorithm. This model combines a multi-scale spatial-temporal convolution module with a spatial-temporal-enhanced strategy, a convolutional auto-encoder for information reconstruction, and a long short-term memory with self-attention, enabling the comprehensive extraction and fusion of EEG features across fine-grained time-frequency variations and dynamic spatial-temporal patterns.

RESULTS: The proposed FN-SSIR achieved a classification accuracy of 86.7% ± 6.6% on our force variation MI dataset, and 78.4% ± 13.0% on the BCI Competition IV 2a dataset.

DISCUSSION: These findings highlight the potential of this paradigm and algorithm for advancing MI-BCI systems in rehabilitation training based on dynamic force interactions.},
}

@article {pmid40620639,
year = {2023},
author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and , and Varol, E and Hurwitz, C and Paninski, L},
title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.},
journal = {Advances in neural information processing systems},
volume = {36},
number = {},
pages = {77604-77631},
pmid = {40620639},
issn = {1049-5258},
support = {U19 NS123716/NS/NINDS NIH HHS/United States ; },
abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.},
}

@article {pmid40619564,
year = {2025},
author = {Beressa, G and Feyissa, GT and Murimi, M and Muhammed, AH and Abdulkadir, A and Jema, AT and Alenko, A and Kebede, A and Lencha, B and Sahiledengle, B and Solomon, D and Atlaw, D and Gomora, D and Zenbaba, D and Dibaba, D and Nigussie, E and Nugusu, F and Desta, F and Ejigu, N and Wake, SK and Girma, S and Jidha, TD and Yazew, T and Tadesse, TM and Elala, T and Tekalegn, Y and Belachew, T},
title = {Nutritional status and associated factors among school age children in Southeast Ethiopia using a bayesian analysis approach.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24141},
pmid = {40619564},
issn = {2045-2322},
mesh = {Humans ; Child ; Ethiopia/epidemiology ; *Nutritional Status ; Bayes Theorem ; Adolescent ; Male ; Female ; Cross-Sectional Studies ; *Growth Disorders/epidemiology ; Prevalence ; Body Mass Index ; *Thinness/epidemiology ; Malnutrition/epidemiology ; Schools ; },
abstract = {Undernutrition among school-age children is a major public health concern in sub-Saharan Africa. This study aimed to assess the nutritional status and associated factors among school-age children in the hard-to-reach pastoral communities in Southeast Ethiopia. We conducted a school-based cross-sectional study among 395 randomly selected schoolchildren aged 7-14 years in pastoral communities in Bale Zone. We employed a hybrid of multistage sampling and systematic random sampling to select the respondents. We used the Z scores of height for age (HAZ) and body mass index for age (BAZ) based on the World Health Organization (WHO) guidance to classify nutritional status of the school-age children. We conducted a Bayesian linear regression analysis estimation using Markov chain Monte Carlo (MCMC). We calculated the mean, along with a 95% Bayesian credible interval (BCI), to identify factors associated with nutritional status. The overall prevalence of stunting and thinness among school-age children 7-14 years was 26.6% (95% CI: 21.8, 31.4%) and 28.9% (95% CI: 24.3, 33.2%), respectively. The mean and SD of HAZ and BAZ scores were -0.82 (2.13) and -0.87 (1.73), respectively. A unit increment in the age of the child and a unit increment in dietary diversity score were associated with an increment in HAZ scores by 0.122 and 0.120 units, respectively. Travelling to school for more than 30 min and more (compared to travelling less than 30 min) and being a child of a literate father (compared to being a child of an illiterate father) were associated with a decrement in the mean HAZ scores by 0.81 and 0.675 units, respectively. Children who come from rich families had BAZ scores, which are about 0.50 units higher when compared to those children coming from poor families. The high burden of stunting and thinning among the hard-to-reach pastoral communities underscores the importance of strengthening nutrition intervention programs such as school feeding and multisectoral collaboration and economic empowerment to improve accessibility of diversified food among school-age children in the hard-to-reach pastoral communities. Younger school children, children from poor families and children who have less access to school and diverse diets should be prioritised during school based nutritional interventions.},
}

Humans
Child
Ethiopia/epidemiology
*Nutritional Status
Bayes Theorem
Adolescent
Male
Female
Cross-Sectional Studies
*Growth Disorders/epidemiology
Prevalence
Body Mass Index
*Thinness/epidemiology
Malnutrition/epidemiology
Schools

@article {pmid40616172,
year = {2025},
author = {Ji, X and Zhang, J and Chen, D and Qin, Q and Huang, F},
title = {Research on transcranial magnetic stimulation for stroke rehabilitation: a visual analysis based on CiteSpace.},
journal = {European journal of medical research},
volume = {30},
number = {1},
pages = {575},
pmid = {40616172},
issn = {2047-783X},
support = {No.CRSI2022CZ-17//China Rehabilitation Research Center under the Central Public Welfare Scientific Research Institute Basic Research Business Fund Project/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; *Stroke/therapy ; Bibliometrics ; },
abstract = {OBJECTIVE: This study aimed to analyze recent research and emerging trends in transcranial magnetic stimulation (TMS) for stroke rehabilitation.

METHODS: We employed bibliometric methods to retrieve relevant Chinese and English literature on TMS for stroke rehabilitation from China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WOSCC) respectively, including publications up to April 10, 2025. CiteSpace 6.4.R1 was utilized to generate knowledge maps, focusing on authors, institutions, countries, and keywords.

RESULTS: We identified 1301 publications since the inception of the database through April 10, 2025, including 797 articles in Chinese and 504 articles in English. The number of articles available in both languages increased over time. Fudan University and University of Manchester were the institutions with the most outputs. Co-occurrence and clustering keyword analyses revealed similarities between Chinese and English terms, with key research areas include the role of TMS in motor cortex areas, post-stroke cognitive impairment (PSCI), and dysphagia, and TMS has been integrated with other therapeutic approaches for stroke patients.

CONCLUSION: TMS, a noninvasive brain stimulation technique, has been applied to improve stroke patients' functional outcomes and daily living skills. Future investigations should integrate TMS with cutting-edge technologies including artificial intelligence and brain‒computer interfaces to uncover its full potential in restoring neural function in stroke survivors.},
}

Humans
*Transcranial Magnetic Stimulation/methods
*Stroke Rehabilitation/methods
*Stroke/therapy
Bibliometrics

@article {pmid40615688,
year = {2025},
author = {Li, Y and Wang, YJ and Su, C and Deng, F and Pan, Y},
title = {Bidirectional information flow in cooperative learning reflects emergent leadership.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1000},
pmid = {40615688},
issn = {2399-3642},
support = {Nos. 62207025, 62337001//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. LMS25C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Leadership ; Male ; Female ; *Learning/physiology ; *Cooperative Behavior ; Adult ; Spectroscopy, Near-Infrared ; Young Adult ; *Brain/physiology ; },
abstract = {Advances in social neuroscience have shown that one of the fundamental characteristics of cooperative learning is synchronization between learners' brains. However, the directionality of this synchronization, and the role of emergent leadership (i.e., a group leader emerges naturally), in cooperative learning remain unclear. Here, we investigated the directionality and dynamics of information flow by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning and Granger causality analysis (GCA). Through a 6 min dyadic cooperative learning task, we observed that dyads' utterance score increased over time and remained stable at the end of interaction, suggesting successful cooperative learning. At the neural level, we found a stronger leader-to-follower Granger causality in the left middle temporal gyrus, alongside a more pronounced follower-to-leader causality in the left sensorimotor cortex. Moreover, we found that information transfer in both directions increased and peaked around the first half of time into the task, followed by a decline. These temporally similar yet spatially dissociable patterns of directional information flow suggest a hierarchical organization of bidirectional communication during cooperative learning with emergent leadership.},
}

Humans
*Leadership
Male
Female
*Learning/physiology
*Cooperative Behavior
Adult
Spectroscopy, Near-Infrared
Young Adult
*Brain/physiology

@article {pmid40615618,
year = {2025},
author = {Hobbs, FDR and Dorward, J and Hayward, G and Yu, LM and Saville, BR and Butler, CC and , },
title = {The PRINCIPLE randomised controlled open label platform trial of hydroxychloroquine for treating COVID19 in community based patients at high risk.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23850},
pmid = {40615618},
issn = {2045-2322},
support = {CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; },
mesh = {Humans ; *Hydroxychloroquine/therapeutic use/adverse effects/administration & dosage ; *COVID-19 Drug Treatment ; Female ; Male ; Aged ; Middle Aged ; United Kingdom/epidemiology ; SARS-CoV-2 ; COVID-19/virology ; *Antiviral Agents/therapeutic use ; Hospitalization/statistics & numerical data ; Prospective Studies ; Aged, 80 and over ; Treatment Outcome ; },
abstract = {Early on in the COVID-19 pandemic, we aimed to assess the effectiveness of hydroxychloroquine on reducing the need for hospital admission in patients in the community at higher risk of complications from COVID-19 syndromic illness (testing was largely unavailable at the time, hence not microbiologically confirmed SARS-CoV-2 infection), as part of the national open-label, multi-arm, prospective, adaptive platform, randomised clinical trial in community care in the United Kingdom (UK). People aged 65 and over, or aged 50 and over with comorbidities, and who had been unwell for up to 14 days with suspected COVID-19 were randomised to usual care with the addition of hydroxychloroquine, 200 mg twice a day for seven days, or usual care without hydroxychloroquine (control). Participants were recruited based on symptoms and approximately 5% had confirmed SARS-COV2 infection. The primary outcome while hydroxychloroquine was in the trial was hospital admission or death related to suspected COVID-19 infection within 28 days from randomisation. First recruitment was on April 2, 2020, and the hydroxychloroquine arm was suspended by the UK Medicines Regulator on May 22, 2020. 207 were randomised to hydroxychloroquine and 206 to usual care, and 190 and 194 contributed to the primary analysis results presented, respectively. There was no swab result available within 28 days of randomisation for 39% in both groups: 107 (54%) in the hydroxychloroquine group and 111 (55%) in the usual care group tested negative for SARS-Cov-2, and 13 (7%) and 11 (5%) tested positive. 13 participants, (seven (3·7%) in the usual care plus hydroxychloroquine and six (3.1%) in the usual care group were hospitalized (odds ratio 1·04 [95% BCI 0·36 to 3.00], probability of superiority 0·47). There was one serious adverse event, in the usual care group. More people receiving hydroxychloroquine reported nausea. We found no evidence from this treatment arm of the PRINCIPLE trial, stopped early and therefore under-powered for reasons external to the trial, that hydroxychloroquine reduced hospital admission or death in people with suspected, but mostly unconfirmed COVID-19.},
}

Humans
*Hydroxychloroquine/therapeutic use/adverse effects/administration & dosage
*COVID-19 Drug Treatment
Female
Male
Aged
Middle Aged
United Kingdom/epidemiology
SARS-CoV-2
COVID-19/virology
*Antiviral Agents/therapeutic use
Hospitalization/statistics & numerical data
Prospective Studies
Aged, 80 and over
Treatment Outcome

@article {pmid40615558,
year = {2025},
author = {Xi, C and Lu, B and Guo, X and Qin, Z and Yan, C and Hu, S},
title = {Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40615558},
issn = {1476-5578},
abstract = {Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r(df=147)=0.4493, p = 2*10[-4]). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.},
}

@article {pmid40614757,
year = {2025},
author = {Mishler, JH and Yun, R and Perlmutter, S and Rao, RPN and Fetz, EE},
title = {Manipulation of neuronal activity by an artificial spiking neural network implemented on a closed-loop brain-computer interface in non-human primates.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adec1c},
pmid = {40614757},
issn = {1741-2552},
abstract = {OBJECTIVE: Closed-loop brain-computer interfaces (clBCIs) can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks (SNNs) can be bidirectionally interfaced with single neurons (SNs) in the neocortex of non-human primates (NHPs) to create artificial connections between the SNs to manipulate their activity in predictable ways.

APPROACH: Spikes from a small group of SNs were recorded from primary motor cortex of two awake NHPs during rest. The SNs were then interfaced with a small network of integrate-and-fire units (IFUs) that were programmed on a custom clBCI. Spikes from the SNs evoked excitatory and/or inhibitory postsynaptic potentials (EPSPs/IPSPs) in the IFUs, which themselves spiked when their membrane potentials exceeded a predetermined threshold. Spikes from the IFUs triggered single pulses of intracortical microstimulation (ICMS) to modulate the activity of the cortical SNs.

MAIN RESULTS: We show that the altered closed-loop dynamics within the cortex depends on several factors including the connectivity between the SNs and IFUs, as well as the precise timing of the ICMS. We additionally show that the closed-loop dynamics can reliably be modeled from open-loop measurements.

SIGNIFICANCE: Our results demonstrate a new type of hybrid biological-artificial neural system based on a clBCI that interfaces SNs in the brain with artificial IFUs to modulate biological activity in the brain. Our model of the closed-loop dynamics may be leveraged in the future to develop training algorithms that shape the closed-loop dynamics of networks in the brain to correct aberrant neural activity and rehabilitate damaged neural circuits.},
}

@article {pmid40614457,
year = {2025},
author = {Yan, H and Wang, Z and Li, J},
title = {MSC-transformer-based 3D-attention with knowledge distillation for multi-action classification of separate lower limbs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107806},
doi = {10.1016/j.neunet.2025.107806},
pmid = {40614457},
issn = {1879-2782},
abstract = {Deep learning has been extensively applied to motor imagery (MI) classification using electroencephalogram (EEG). However, most existing deep learning models do not extract features from EEG using dimension-specific attention mechanisms based on the characteristics of each dimension (e.g., spatial dimension), while effectively integrate local and global features. Furthermore, implicit information generated by the models has been ignored, leading to underutilization of essential information of EEG. Although MI classification has been relatively thoroughly investigated, the exploration of classification including real movement (RM) and motor observation (MO) is very limited, especially for separate lower limbs. To address the above problems and limitations, we proposed a multi-scale separable convolutional Transformer-based filter-spatial-temporal attention model (MSC-T3AM) to classify multiple lower limb actions. In MSC-T3AM, spatial attention, filter and temporal attention modules are embedded to allocate appropriate attention to each dimension. Multi-scale separable convolutions (MSC) are separately applied after the projections of query, key, and value in self-attention module to improve computational efficiency and classification performance. Furthermore, knowledge distillation (KD) was utilized to help model learn suitable probability distribution. The comparison results demonstrated that MSC-T3AM with online KD achieved best performance in classification accuracy, exhibiting an elevation of 2 %-19 % compared to a few counterpart models. The visualization of features extracted by MSC-T3AM with online KD reiterated the superiority of the proposed model. The ablation results showed that filter and temporal attention modules contributed most for performance improvement (improved by 2.8 %), followed by spatial attention module (1.2 %) and MSC module (1 %). Our study also suggested that online KD was better than offline KD and the case without KD. The code of MSC-T3AM is available at: https://github.com/BICN001/MSC-T3AM.},
}

@article {pmid40611671,
year = {2025},
author = {Alemu, RZ and Blakeman, A and Fung, AL and Hazen, M and Negandhi, J and Papsin, BC and Cushing, SL and Gordon, KA},
title = {Children With Bilateral Cochlear Implants Show Emerging Spatial Hearing of Stationary and Moving Sound.},
journal = {Trends in hearing},
volume = {29},
number = {},
pages = {23312165251356333},
doi = {10.1177/23312165251356333},
pmid = {40611671},
issn = {2331-2165},
mesh = {Humans ; *Sound Localization ; *Cochlear Implants ; Child ; Male ; Female ; *Cochlear Implantation/instrumentation ; Auditory Threshold ; Speech Perception ; Adolescent ; Cues ; Acoustic Stimulation ; *Persons with Hearing Disabilities/rehabilitation/psychology ; Case-Control Studies ; Eye Movements ; Noise/adverse effects ; Head Movements ; *Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology ; },
abstract = {Spatial hearing in children with bilateral cochlear implants (BCIs) was assessed by: (a) comparing localization of stationary and moving sound, (b) investigating the relationship between sound localization and sensitivity to interaural level and timing differences (ILDs/ITDs), (c) evaluating effects of aural preference on sound localization, and (d) exploring head and eye (gaze) movements during sound localization. Children with BCIs (n = 42, MAge = 12.3 years) with limited duration of auditory deprivation and peers with typical hearing (controls; n = 37, MAge = 12.9 years) localized stationary and moving sound with unrestricted head and eye movements. Sensitivity to binaural cues was measured by a lateralization task to ILDs and ITDs. Spatial separation effects were measured by spondee-word recognition thresholds (SNR thresholds) when noise was presented in front (colocated/0°) or with 90° of left/right separation. BCI users had good speech reception thresholds (SRTs) in quiet but higher SRTs in noise than controls. Spatial separation of noise from speech revealed a greater advantage for the right ear across groups. BCI users showed increased errors localizing stationary sound and detecting moving sound direction compared to controls. Decreased ITD sensitivity occurred with poorer localization of stationary sound in BCI users. Gaze movements in BCI users were more random than controls for stationary and moving sounds. BCIs support symmetric hearing in children with limited duration of auditory deprivation and promote spatial hearing which is albeit impaired. Spatial hearing was thus considered to be "emerging." Remaining challenges may reflect disruptions in ITD sensitivity and ineffective gaze movements.},
}

Humans
*Sound Localization
*Cochlear Implants
Child
Male
Female
*Cochlear Implantation/instrumentation
Auditory Threshold
Speech Perception
Adolescent
Cues
Acoustic Stimulation
*Persons with Hearing Disabilities/rehabilitation/psychology
Case-Control Studies
Eye Movements
Noise/adverse effects
Head Movements
*Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology

@article {pmid40611622,
year = {2025},
author = {Dahò, M and Monzani, D},
title = {The multifaceted nature of inner speech: Phenomenology, neural correlates, and implications for aphasia and psychopathology.},
journal = {Cognitive neuropsychology},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/02643294.2025.2527983},
pmid = {40611622},
issn = {1464-0627},
abstract = {This narrative review explores the phenomenon of inner speech - mental speech without visible articulation - and its implications for cognitive science and clinical practice. Despite its importance, the many neural mechanisms underlying inner speech remain unclear. We propose classifying inner speech into monologic, dialogal, elicited, and spontaneous forms, and discuss related phenomenological and neural correlates theories. A literature review on PubMed (1990-2024) identified 83 studies. Dialogal forms recruit Theory of Mind networks, compared to monologic forms. Task-elicited inner speech activates the left inferior frontal gyrus more strongly, while spontaneous inner speech engages Heschl's gyrus, suggesting auditory involvement. Evidence regarding aphasia suggests inner speech may be partially preserved even when overt speech is impaired, offering a potential route for rehabilitation. Future research should also address the emotional aspects of inner speech, its role in psychopathology, and its developmental trajectory. Such studies may improve interventions for disorders related to dysfunctional inner speech.Abbreviation: ACC: anterior cingulate cortex; ALE: activation likelihood estimation; AVH: auditory verbal hallucination; BMI: brain-machine interface; CD: corollary discharge; ConDialInt: consciousness-dialogue-intentionality; DES: descriptive experience sampling; DTI: diffusion tensor imaging; dPMC: dorsal premotor cortex; dmPFC: dorsomedial prefrontal cortex; IFG: inferior frontal gyrus; M1: primary motor cortex; MedFG: medial frontal gyrus; MFG: middle frontal gyrus; MTG: middle temporal gyrus; MRI: magnetic resonance imaging; preSMA: presupplementary motor area; PrG: precentral gyrus; SMA: supplementary motor area; SMG: supramarginal gyrus; SPC: superior parietal cortex; SPL: superior parietal lobule; STG: superior temporal gyrus; STS: superior temporal sulcus; TVA: temporal vocal areas; ToM: theory of mind; vmPFC: ventromedial prefrontal cortex.},
}

@article {pmid40611619,
year = {2025},
author = {Ponomarev, T and Vasilyev, A and Novikova, E and Pokidko, A and Zaitseva, N and Zaitsev, D and Kaplan, A},
title = {Brain mechanisms of (dis)agreement: ERP evidence from binary choice responses.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf167},
pmid = {40611619},
issn = {1460-2199},
support = {121032300070-1//Lomonosov Moscow State University/ ; },
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; Electroencephalography ; *Brain/physiology ; Young Adult ; Adult ; *Choice Behavior/physiology ; Brain-Computer Interfaces ; *Decision Making/physiology ; },
abstract = {Agreement and disagreement are essential brain processes that enable effective communication and decision-making. However, a clear neurophysiological framework explaining their organization is still lacking. The present study aimed to identify EEG correlates of implicit agreement and disagreement, developing a novel experimental paradigm to model these internal responses. Participants were tasked with mentally responding to binary ("yes" or "no") questions and evaluating the accuracy of a computer system's attempts to "guess" their responses. Event-related potentials (ERP) revealed distinct patterns associated with agreement and disagreement in two key contexts: when participants read the final word of a question and when they observed the computer's "guess." Disagreement, compared to agreement, elicited larger ERP amplitudes, specifically an enhanced N400 component in the first context and increased feedback-related negativity in the second. Considering the associations of these ERP components with cognitive processes, this research offers robust evidence linking agreement and disagreement to the brain's effort in reconciling personal beliefs and expectations with new information. Furthermore, the experimental framework and findings provide a foundation for the development of brain-computer interfaces (BCIs) capable of detecting "yes" and "no" commands based on their intrinsic EEG predictors, offering promising applications in assistive technologies and neural communication systems.},
}

Humans
Male
Female
*Evoked Potentials/physiology
Electroencephalography
*Brain/physiology
Young Adult
Adult
*Choice Behavior/physiology
Brain-Computer Interfaces
*Decision Making/physiology

@article {pmid40611612,
year = {2025},
author = {Saeed, S and Wang, H and Jia, M and Liu, TT and Xu, L and Zhang, X and Hu, SH},
title = {The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes.},
journal = {Annals of medicine},
volume = {57},
number = {1},
pages = {2517813},
doi = {10.1080/07853890.2025.2517813},
pmid = {40611612},
issn = {1365-2060},
mesh = {Humans ; *Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology ; *Demyelinating Diseases/diagnosis/therapy/immunology/complications ; Autoantibodies ; Treatment Outcome ; },
abstract = {BACKGROUND: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management.

METHODS: Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes.

RESULTS: Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR + MOGAD subgroup exhibited seizures (51-72.7%), psychiatric symptoms (45.5-71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies.

CONCLUSIONS: Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.},
}

Humans
*Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology
*Demyelinating Diseases/diagnosis/therapy/immunology/complications
Autoantibodies
Treatment Outcome

@article {pmid40611081,
year = {2025},
author = {Wei, Y and Xu, Y and Chen, W and Zheng, J and Chen, H and Chen, S},
title = {Can heart rate variability demonstrate the effects and the levels of mindfulness? A repeated-measures study on experienced and novice mindfulness practitioners.},
journal = {BMC complementary medicine and therapies},
volume = {25},
number = {1},
pages = {231},
pmid = {40611081},
issn = {2662-7671},
mesh = {Humans ; *Heart Rate/physiology ; *Mindfulness ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Meditation ; },
abstract = {BACKGROUND: Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners.

METHODS: Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20 min baseline, T1; 20 min mindfulness, T2; 20 min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data.

RESULTS: The experienced group had higher full-scale scores of FFMQ both in the pre-test (t = -3.34, df = 44, p = 0.002) and the post-test (t = -2.35, df = 44, p = 0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p < 0.05). In the experienced group, significant fluctuations (p < 0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_α1, and DFA_α2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_α1.

CONCLUSIONS: This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.},
}

Humans
*Heart Rate/physiology
*Mindfulness
Male
Female
Adult
Young Adult
Middle Aged
Meditation

@article {pmid40609489,
year = {2025},
author = {Cui, H and Hu, D and Yang, T and Huang, C and Yang, Z and Dong, S},
title = {Humidity sensors based on surface-functionalized tunable photonic crystal grating.},
journal = {Talanta},
volume = {296},
number = {},
pages = {128521},
doi = {10.1016/j.talanta.2025.128521},
pmid = {40609489},
issn = {1873-3573},
abstract = {Photonic crystal (PC)-based humidity sensors detect changes in humidity using periodic structural color variations and have significant potential in the humidity detection field. However, current technologies typically rely on observing these structural color changes with the human eye. The human eye has limited color discrimination, thus resulting in insufficient detection accuracy. Meanwhile, viewing angles and ambient lighting can also disrupt observations. Here, we propose a humidity sensor based on surface-functionalized tunable PC grating. The tunable PC grating consists of a 600 nm polystyrene (PS) microsphere PC and a humidity-sensitive hydrogel. As ambient humidity increases, the hydrophilic amide groups (-CONH2) inside the hydrogel interact with the hydrogen bonds between water molecules and triggers hydrogel swelling, exerts interfacial stress on the PS microsphere lattice, thus expanding the lattice spacing of the PS microspheres and causing a red shift in the reflected wavelength. Integrating the surface-functionalized tunable PC grating into a Czerny-Turner (C-T) optical system enables us to directly translate humidity into precise spectral shifts, overcoming the limitations of human eye-based observations. Experimental results demonstrate a strong linear response over the range of 24-94 % relative humidity (RH), as well as excellent repeatability and long-term stability. We provide an innovative solution for high-precision optical humidity sensing.},
}

@article {pmid40609413,
year = {2025},
author = {Wang, Y and Gao, Y and He, R and Gao, Y and Xu, Z and Wang, C and Liu, F},
title = {Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance.},
journal = {The Science of the total environment},
volume = {993},
number = {},
pages = {179856},
doi = {10.1016/j.scitotenv.2025.179856},
pmid = {40609413},
issn = {1879-1026},
abstract = {CO2 exchange at air-sea interface is crucial for global carbon cycle. Uncertainties in CO2 flux quantification are constrained by ocean surface partial pressure of CO2 (pCO2) variations. While regional pCO2 retrieval algorithms exist, the impact of mesoscale eddies on accuracy remains understudies. We improve the global ocean surface pCO2 retrieval algorithm using XGBoost, incorporating sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), mixed layer depth (MLD), and sea surface height (SSH), achieving high performance (R[2]= 0.95, RMSE = 10.52 μatm) at daily resolution. The SHAP method and the sequential feature removal method were used to assesses the individual impacts. The results reveal that SSH significantly enhances model accuracy, increasing R[2] by ∼10% and decreasing RMSE by ∼38%. Regional evaluations show better performance in the Atlantic, with overestimation (underestimation) at ocean gyre fronts (interiors). The models perform better in summer, while in winter, more overestimation is observed in the North Pacific. The future prediction in global field shows excellent spatiotemporal extrapolation performance. The results verify mesoscale dynamics significantly impact the retrieval accuracy in energetic regions. Relative error normalized quantities were calculated for cyclonic and anticyclonic eddies in eddy-active regions to analyze the influence of energetic mesoscale dynamic, suggesting that regional and seasonal variations in errors are linked to differences in eddy-induced nutrient flux and baroclinic instabilities.},
}

@article {pmid40609325,
year = {2025},
author = {Ren, X and Zhou, C and Jiang, Y and Zhao, J and Tina, X and Xu, N and Fu, M and Ni, P and Li, T and Zhang, X},
title = {Generation of an induced pluripotent stem cell line (HZSMHCi002-A) from a patient with neuronal intranuclear inclusion disease carrying GGC repeat expansion in the NOTCH2NLC gene.},
journal = {Stem cell research},
volume = {87},
number = {},
pages = {103761},
doi = {10.1016/j.scr.2025.103761},
pmid = {40609325},
issn = {1876-7753},
abstract = {The NOTCH2NLC gene contains a GGC repeat expansion in its 5' untranslated region. This expansion is associated with neuronal intranuclear inclusion disease (NIID). NIID is a rare neurodegenerative disorder. Its clinical features include cognitive decline, paroxysmal symptoms, and autonomic dysfunction. We generated an induced pluripotent stem cell (iPSC) line from a female patient's PBMCs carrying a high GGC repeat expansion in NOTCH2NLC. The iPSC line displayed typical pluripotent morphology. It expressed key pluripotency markers and demonstrated differentiation potential in teratoma assays. This cell line serves as a useful model for studying disease mechanisms and developing therapeutic strategies.},
}

@article {pmid40609285,
year = {2025},
author = {Xu, JJ and Chen, YL and Yu, H and Chen, DF and Li, HF and Wu, ZY},
title = {Genetic and Clinical Features of SLC2A1-Related Paroxysmal Exercise-Induced Dyskinesia.},
journal = {Pediatric neurology},
volume = {170},
number = {},
pages = {31-37},
doi = {10.1016/j.pediatrneurol.2025.06.006},
pmid = {40609285},
issn = {1873-5150},
abstract = {BACKGROUND: Paroxysmal exercise-induced dyskinesia (PED) is a rare movement disorder characterized by choreoathetosis and dystonia triggered by sustained exercise, commonly affecting the lower extremities. PED is an autosomal dominant disorder genetically linked to mutations in the SLC2A1 gene. The transmembrane protein Glut1, encoded by the SLC2A1 gene, can transport glucose from blood to the brain. This study aimed to characterize the genetic and clinical features of SLC2A1-related PED.

METHODS: We reported two Chinese PED families presenting with involuntary movements after prolonged exercise. Whole-exome sequencing was performed on two probands, and cosegregation analysis was subsequently carried out in available family members. Additionally, we summarized and analyzed the genetic and clinical features of SLC2A1-related PED by retrieving information from the literature.

RESULTS: Genetic testing identified two missense mutations in SLC2A1 in these families, including a known disease-causing mutation, c.997C>T (p.R333W), and a novel mutation, c.823G>C (p.A275P). Upon review of the literature, mutations in certain regions of the Glut1 protein, particularly in transmembrane segments 3, 4, 5, 7, and 8, together with the intracellular domain, were more frequently seen in PED. Among the various types of epilepsy, absence seizures were the most common in patients with PED. Furthermore, familial PED had a later onset and a higher cerebrospinal fluid/blood glucose ratio. Patients with missense mutations exhibited a later onset than those with truncated mutations.

CONCLUSIONS: Our study identified a new disease-causing mutation and, through an extensive literature review, provided a detailed genetic and clinical description of PED associated with SLC2A1 mutations.},
}

@article {pmid40608885,
year = {2025},
author = {Yang, Z and Si, X and Jin, W and Huang, D and Zang, Y and Yin, S and Ming, D},
title = {SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3585528},
pmid = {40608885},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology for emotion recognition holds significant potential for future applications in the treatment of refractory emotional disorders. Stereo-electroencephalography (SEEG), being less invasive, can precisely record neural activities originating from the cortex and the deep structures of the brain. Thus, it has broad application prospects in constructing emotion recognition BCI. In this study, SEEG data from nine subjects were collected to construct an emotion dataset, and a Spatial Transformer-based Hybrid Network (STHN) was proposed for SEEG emotion recognition. The triple-classification accuracy of STHN reached 83.56%, outperforming the baseline methods such as EEGNet, TSception, and the deep convolution neural network. Moreover, STHN can assign weights to each SEEG channel and select those channels that contribute more significantly to emotion recognition. It was found that when using the top 30% weighted SEEG channels as model inputs, the accuracy did not decrease significantly. Most of the channels with higher weights were located in brain regions strongly associated with emotions, such as the frontal lobe, the temporal lobe, and the hippocampus. This indicates that STHN is not merely a "black-box" model but possesses a degree of explainability. To the best of our knowledge, this is the first study to develop an SEEG emotion recognition algorithm, which is expected to play a crucial role in the monitoring and treatment of patients with refractory emotional disorders in the future.},
}

@article {pmid40608881,
year = {2025},
author = {Yu, X and Yu, X},
title = {Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2025.3580726},
pmid = {40608881},
issn = {2168-2275},
abstract = {Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.},
}

@article {pmid40606836,
year = {2025},
author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Arias-Carrión, O and Ortega-Robles, E and Galicia-Alvarado, MA and Valdés-Cristerna, R and Ramirez-Nava, AG and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Pacheco-Gallegos, MDR and Marín-Arriaga, N and Carino-Escobar, RI},
title = {The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1579988},
pmid = {40606836},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interfaces (BCI) are a promising complementary therapy for stroke rehabilitation due to the close-loop feedback that can be provided with these systems, but more evidence is needed regarding their clinical and neuroplasticity effects.

METHODS: A randomized controlled trial was performed using the ReHand-BCI system that provides feedback with a robotic hand orthosis. The experimental group (EG) used the ReHand-BCI, while sham-BCI was given to the control group (CG). Both groups performed 30 therapy sessions, with primary outcomes being the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Secondary outcomes were hemispheric dominance, measured with electroencephalography and functional magnetic resonance imaging, white matter integrity via diffusion tensor imaging, and corticospinal tract integrity and excitability, measured with transcranial magnetic stimulation.

RESULTS: At post-treatment, patients in both groups had significantly different FMA-UE scores (EG: baseline = 24.5[20, 36], post-treatment 28[23, 43], CG: baseline = 26[16, 37.5], post-treatment = 34[17.3, 46.5]), while only the EG had significantly different ARAT scores at post-treatment (EG: baseline = 8.5[5, 26], post-treatment = 20[7, 36], CG: baseline = 3[1.8, 30.5], post-treatment = 15[2.5, 40.8]). In addition, across the intervention, the EG showed trends of more pronounced ipsilesional cortical activity and higher ipsilesional corticospinal tract integrity, although these differences were not statistically different compared to the control group, likely due to the study's sample size.

CONCLUSION: To the authors' knowledge, this is the first clinical trial that has assessed such a wide range of physiological effects across a long BCI intervention, implying that a more pronounced ipsilesional hemispheric dominance is associated with upper extremity motor recovery. Therefore, the study brings light into the neuroplasticity effects of a closed-loop BCI-based neurorehabilitation intervention in stroke.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier NCT04724824.},
}

@article {pmid40606655,
year = {2025},
author = {Jacob, JE and Chandrasekharan, S},
title = {Editorial: Advanced EEG analysis techniques for neurological disorders.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1637890},
doi = {10.3389/fninf.2025.1637890},
pmid = {40606655},
issn = {1662-5196},
}

@article {pmid40605914,
year = {2025},
author = {Yang, L and Zhu, W},
title = {Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {106},
pmid = {40605914},
issn = {1871-4080},
abstract = {Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.},
}

@article {pmid40603471,
year = {2025},
author = {Liao, W and Liu, H and Wang, W},
title = {Advancing BCI with a transformer-based model for motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23380},
pmid = {40603471},
issn = {2045-2322},
support = {2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Imagination/physiology
Neural Networks, Computer
Algorithms
Machine Learning
Deep Learning

@article {pmid40603333,
year = {2025},
author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD},
title = {Correction: A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1132},
doi = {10.1038/s41597-025-05466-y},
pmid = {40603333},
issn = {2052-4463},
}

@article {pmid40602422,
year = {2025},
author = {Jin, J and Liang, W and Xu, R and Chen, W and Xu, R and Wang, X and Cichocki, A},
title = {A transformer-based network with second-order pooling for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeae8},
pmid = {40602422},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks (CNNs), have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.

APPROACH: To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.

MAIN RESULTS: SecTNet is evaluated on two publicly available EEG datasets, namely BCI Competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI Competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.

SIGNIFICANCE: These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.},
}

@article {pmid40602419,
year = {2025},
author = {Li, L and Wei, B},
title = {A two-stage EEG zero-shot classification algorithm guided by class reconstruction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeaea},
pmid = {40602419},
issn = {1741-2552},
abstract = {Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram(EEG) signals have garnered widespread attention recently due to their noninvasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface(BCI) research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The Contrastive Language-Image Pre-training(CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability. The method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively. These results further validate the effectiveness of the proposed method in EEG zero-shot classification.},
}

@article {pmid40602315,
year = {2025},
author = {Del Pup, F and Zanola, A and Tshimanga, LF and Bertoldo, A and Finos, L and Atzori, M},
title = {The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110608},
doi = {10.1016/j.compbiomed.2025.110608},
pmid = {40602315},
issn = {1879-0534},
abstract = {Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal properties (e.g., biometric). Such variability in model evaluation leads to incomparable studies and, increasingly, overestimated performance claims, which are detrimental to the field. Nevertheless, no comprehensive guidelines for proper data partitioning and cross-validation exist in the domain, nor is there a quantitative evaluation of the impact of different approaches on model accuracy, reliability, and generalizability. To assist researchers in identifying optimal experimental strategies, this paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models. Five cross-validation settings are compared across three supervised cross-subject classification tasks (brain-computer interfaces, Parkinson's, and Alzheimer's disease classification) and four established architectures of increasing complexity (ShallowConvNet, EEGNet, DeepConvNet, and Temporal-based ResNet). The comparison of over 100,000 trained models underscores, first, the importance of using subject-based cross-validation strategies for evaluating EEG deep learning architectures, except when within-subject analyses are acceptable (e.g., BCI). Second, it highlights the greater reliability of nested approaches (e.g., N-LNSO) compared to non-nested counterparts, which are prone to data leakage and favor larger models overfitting to validation data. In conclusion, this work provides EEG deep learning researchers with an analysis of data partitioning and cross-validation and offers guidelines to avoid data leakage, currently undermining the domain with potentially overestimated performance claims.},
}

@article {pmid40602314,
year = {2025},
author = {Huang, S and Wei, Q},
title = {A deep learning model combining convolutional neural networks and a selective kernel mechanism for SSVEP-Based BCIs.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110691},
doi = {10.1016/j.compbiomed.2025.110691},
pmid = {40602314},
issn = {1879-0534},
abstract = {Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing global temporal features due to limited receptive fields. To address these challenges, we propose a novel deep learning model, FBCNN-TKS, which extracts harmonic components from SSVEP signals using a filter bank technique, followed by feature extraction through convolutional neural networks (CNNs) and a temporal kernel selection (TKS) module, and finally the weighted sum of cross-entropy loss and center loss is used as the objective function for model optimization. The key innovation of our approach lies in the introduction of the TKS module, which significantly enhances feature extraction capability by providing a broader receptive field. Additionally, dilated and grouped convolutions are used in TKS module to reduce the number of model parameters, minimizing the risk of overfitting and improving classification accuracy. Experimental results manifest that FBCNN-TKS outperforms state-of-the-art methods in terms of classification accuracy and information transfer rate (ITR). Specifically, FBCNN-TKS achieved the highest ITRs of 251.54 bpm and 203.47 bpm with the highest accuracies of 83.10 % and 72.98 % on public datasets Benchmark and BETA respectively at the data length of 0.4s, exhibiting superior performance. The FBCNN-TKS model bears big potential for the development of high-performance SSVEP-BCI character spelling systems.},
}

@article {pmid40601454,
year = {2025},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Chen, X and Liu, J and Zeng, LL and Hu, D},
title = {DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3581991},
pmid = {40601454},
issn = {2162-2388},
abstract = {Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%-50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%-6.14% in intrasession classification balanced accuracy across 2-6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain-computer communication and control, and other clinical practice.},
}

@article {pmid40601441,
year = {2025},
author = {Si, X and Han, Y and Li, S and Zhang, S and Ming, D},
title = {The cortical spatial responses and decoding of emotion imagery towards a novel fNIRS-based affective BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3584765},
pmid = {40601441},
issn = {1558-0210},
abstract = {Functional near-infrared spectroscopy (fNIRS), with its non-invasive and high spatial resolution, holds promise in developing novel affective brain-computer interface (BCI). Similar to motor imagery BCI, emotion imagery BCI could recognize internal emotions and convey them to the external world. This holds clinical value for expressing emotions in patients with neurological impairments and serves as a proactive emotion regulation method. However, the fNIRS features of emotion imagery for affective BCI and the discriminability of different emotion categories remain unclear. Here, this study designed a novel emotion verbal imagery paradigm (imagining descriptions of happy or sad scenes). First, task-related hemodynamic responses were analyzed from 17 subjects. Then, statistical analyses were then conducted to reveal the significant cortical spatial response patterns. Additionally, decoding experiments and model interpretability are employed to assist in validating the feasibility of the emotion imagery BCI. Results showed: (1) Happy imagery recruited frontoparietal regions, such as the left dorsal secondary motor cortex, ventral secondary motor cortex, and inferior parietal lobe. (2) Sad imagery mainly recruited the right dorsolateral prefrontal cortex. (3) The left dorsal sensorimotor cortex exhibited selective responsiveness to happy imagery and sad imagery. (4) The classification results of the emotion imagery task exceeded the random level. (5) Emotional categories activation responses showed significant similarity with the hemodynamic responses of the imagination tasks. Taken together, by proposing the emotion imagery fNIRS paradigm, this work could shed light on the development of feature non-invasive BCI.},
}

@article {pmid40600191,
year = {2025},
author = {Ma Thi, C and Nguyen The, HA and Nguyen Minh, K and Vu Thanh, L and Nguyen Dinh, H and Huynh Thi, NY and Ha Thi, TH and Hoang Tien, TN and Au Dao, DT and Nguyen Hoang, KL and Huynh Kha, V and Le Hoang, TL},
title = {UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1580931},
pmid = {40600191},
issn = {1662-4548},
}

@article {pmid40598468,
year = {2025},
author = {Chen, Y and Zhao, N and Zhang, J and Wu, X and Huang, J and Xu, X and Cai, F and Chen, S and Xu, L and Yan, W and Hong, Y and Wang, Y and Ling, H and Ji, J and Chen, G and Gu, H and Zhang, J and Wu, Q},
title = {Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {373},
pmid = {40598468},
issn = {1741-7015},
mesh = {Humans ; *DNA Methylation ; *Pituitary Neoplasms/genetics/pathology ; *Adenoma/genetics/pathology ; Male ; Female ; Middle Aged ; Adult ; *Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics ; Neoplasm Invasiveness ; Gene Expression Profiling ; },
abstract = {BACKGROUND: Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30-40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.

METHODS: Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.

RESULTS: We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r = 0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene's CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.

CONCLUSIONS: The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).},
}

Humans
*DNA Methylation
*Pituitary Neoplasms/genetics/pathology
*Adenoma/genetics/pathology
Male
Female
Middle Aged
Adult
*Gene Expression Regulation, Neoplastic
Biomarkers, Tumor/genetics
Neoplasm Invasiveness
Gene Expression Profiling

@article {pmid40598460,
year = {2025},
author = {Lu, R and Pang, Z and Gao, T and He, Z and Hu, Y and Zhuang, J and Zhang, Q and Gao, Z},
title = {Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {380},
pmid = {40598460},
issn = {1741-7015},
support = {82372570//the National Science Foundation of China/ ; 82372570//the National Science Foundation of China/ ; 82271422//the National Science Foundation of China/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 22ZR1479000//Shanghai Natural Science Foundation/ ; 20234Y0043//Shanghai Municipal Health Commission/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Aged ; *Feedback, Sensory/physiology ; Chronic Disease ; Magnetic Resonance Imaging ; Adult ; Neuronal Plasticity ; },
abstract = {BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery.

METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery.

RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes.

CONCLUSIONS: Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation.

TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017).},
}

Humans
*Brain-Computer Interfaces
Male
Female
*Stroke Rehabilitation/methods
Middle Aged
*Recovery of Function/physiology
*Stroke/physiopathology
Aged
*Feedback, Sensory/physiology
Chronic Disease
Magnetic Resonance Imaging
Adult
Neuronal Plasticity

@article {pmid40596215,
year = {2025},
author = {Rabbani, M and Sabith, NUS and Parida, A and Iqbal, I and Mamun, SM and Khan, RA and Ahmed, F and Ahamed, SI},
title = {EEG based real time classification of consecutive two eye blinks for brain computer interface applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21007},
pmid = {40596215},
issn = {2045-2322},
mesh = {Humans ; *Blinking/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Support Vector Machine ; Neural Networks, Computer ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of brain-computer interfaces (BCI). This study introduces a novel real-time EEG-based framework for classifying three blink states: no blink, single blink, and two consecutive blinks in one model. EEG data were collected from ten healthy participants using an 8-channel wearable headset under controlled blinking conditions. The data were preprocessed and analyzed using four feature extraction techniques: basic statistical, time-domain, amplitude-driven, and frequency-domain methods. The most significant features were selected to develop three machine learning models: XGBoost, support vector machine (SVM), and neural network (NN). We achieved the highest accuracy of 89.0% for classifying multiple-eye blink detection. To further enhance the model's capacity and suitability for real-life BCI applications, we trained and employed the You Only Look Once (YOLO) model, achieving a recall of 98.67%, a precision of 95.39%, and mAP50 of 99.5%, demonstrating its superior accuracy and robustness in classifying two consecutive eye blinks. In conclusion, this study will be the first groundwork and open a new dimension in EEG-based BCI research by classifying multiple-eye blink detection.},
}

Humans
*Blinking/physiology
*Brain-Computer Interfaces
*Electroencephalography/methods
Male
Adult
Female
Support Vector Machine
Neural Networks, Computer
Young Adult
Machine Learning
Signal Processing, Computer-Assisted
Brain/physiology

@article {pmid40595635,
year = {2025},
author = {Liu, L and Gao, Z and Niu, X and Yu, H and Xin, X and Gu, Y and Ma, G and Gu, Y and Liu, Y and Fang, S and Marquardt, T and Wang, L},
title = {SEMA3B switches axon-axon to axon-glia interactions required for unmyelinated axon envelopment and integrity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5433},
pmid = {40595635},
issn = {2041-1723},
support = {32100758//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Axons/metabolism ; *Semaphorins/metabolism/genetics ; Mice ; Schwann Cells/metabolism ; Hyperalgesia/metabolism ; Male ; Mice, Inbred C57BL ; *Nerve Fibers, Unmyelinated/metabolism ; Peripheral Nerve Injuries/metabolism ; Endocytosis ; *Neuroglia/metabolism ; Cell Communication ; },
abstract = {During peripheral nerve (PN) development, unmyelinated axons (nmAs) tightly fasciculate before being separated and enveloped by non-myelinating Schwann cells (nmSCs), glial cells essential for maintaining nmA integrity. How such a switch from axon-axon to axon-glia interactions is achieved remains poorly understood. Here, we find that inactivating SC-derived SEMA3B or its axonal receptor components in mice leads to incomplete nmA separation and envelopment by nmSCs, eliciting hyperalgesia and allodynia. Conversely, increasing SEMA3B levels in SCs accelerates nmA separation and envelopment. SEMA3B transiently promotes nmA defasciculation accompanied by cell adhesion molecule (CAM) endocytosis, subsequently facilitating nmA-nmSC association. Restoring SEMA3B expression following PN injury promotes nmA-nmSC re-association and alleviates hyperalgesia and allodynia. We propose that SEMA3B-induced CAM turnover facilitates a switch from axon-axon to axon-glia interactions promoting nmA envelopment by nmSCs, which may be exploitable for alleviating PN injury-induced pain by accelerating the restoration of nmA integrity.},
}

Animals
*Axons/metabolism
*Semaphorins/metabolism/genetics
Mice
Schwann Cells/metabolism
Hyperalgesia/metabolism
Male
Mice, Inbred C57BL
*Nerve Fibers, Unmyelinated/metabolism
Peripheral Nerve Injuries/metabolism
Endocytosis
*Neuroglia/metabolism
Cell Communication

@article {pmid40594904,
year = {2025},
author = {Sayem, M and Rafi, MA and Mishu, ID and Mahmud, Z},
title = {Comprehensive genomic analysis reveals virulence and antibiotic resistance genes in a multidrug-resistant Bacillus cereus isolated from hospital wastewater in Bangladesh.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22915},
pmid = {40594904},
issn = {2045-2322},
mesh = {*Bacillus cereus/genetics/pathogenicity/isolation & purification/drug effects ; *Wastewater/microbiology ; Bangladesh ; *Drug Resistance, Multiple, Bacterial/genetics ; Phylogeny ; Hospitals ; Virulence/genetics ; Genome, Bacterial ; Whole Genome Sequencing ; Genomics/methods ; Anti-Bacterial Agents/pharmacology ; Virulence Factors/genetics ; Humans ; },
abstract = {Hospital wastewater represents a significant reservoir for antimicrobial-resistant bacteria, including multidrug-resistant (MDR) Bacillus cereus, a pathogen of growing concern due to its potential impact on public health and environmental safety. This study characterizes the genomic features, antimicrobial resistance (AMR) mechanisms, and virulence potential of Bacillus cereus MBC, isolated from hospital wastewater in Dhaka, Bangladesh. Using whole-genome sequencing (WGS) and advanced bioinformatics, we analyzed the isolate's taxonomy, phylogenetics, functional annotation, and biosynthetic potential. The genome, spanning 5.6 Mb with a GC content of 34.84%, contained 5,881 protein-coding sequences, including 1,424 hypothetical proteins, and 28 genes associated with AMR. Phylogenetic analysis revealed a close genetic relationship with Bacillus cereus ATCC 14579, sharing virulence factors such as hemolysin BL (HBL), non-hemolytic enterotoxin (NHE), and cytotoxin K (CytK), all contributing to its pathogenicity. The ability to form biofilms further enhances the strain's persistence and resistance in hospital environments. AMR profiling identified genes conferring resistance to beta-lactams (e.g., BcI, BcII, BcIII), tetracyclines (tetB(P)), glycopeptides (vanY), and fosfomycin, highlighting the bacterium's capacity to resist a wide array of antibiotics. Functional annotation revealed metabolic pathways involved in iron acquisition and the biosynthesis of siderophores such as petrobactin and bacillibactin, reinforcing the bacterium's adaptability in nutrient-limited environments. Mobile genetic elements, including prophages, CRISPR-Cas systems, and transposable elements, suggest significant horizontal gene transfer (HGT), enhancing genetic plasticity and resistance spread. Pangenomic analysis, involving 125 B. cereus strains, revealed a high degree of genetic diversity and close relationships with strains from clinical, food, and agricultural environments, emphasizing the overlap between clinical and environmental reservoirs of resistance. The strain's isolation from hospital wastewater underscores the complex interplay between environmental contaminants and bacterial evolution, which fosters MDR traits. Our findings underscore the urgent need for enhanced genomic surveillance and wastewater management strategies to mitigate the spread of MDR B. cereus and AMR genes in hospital environments.},
}

*Bacillus cereus/genetics/pathogenicity/isolation & purification/drug effects
*Wastewater/microbiology
Bangladesh
*Drug Resistance, Multiple, Bacterial/genetics
Phylogeny
Hospitals
Virulence/genetics
Genome, Bacterial
Whole Genome Sequencing
Genomics/methods
Anti-Bacterial Agents/pharmacology
Virulence Factors/genetics
Humans

@article {pmid40594760,
year = {2025},
author = {Kanna, RK and Shoran, P and Yadav, M and Ahmed, MN and Burje, S and Shukla, G and Sinha, A and Hussain, MR and Khalid, S},
title = {Improving EEG based brain computer interface emotion detection with EKO ALSTM model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {20727},
pmid = {40594760},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain-computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Emotions/physiology
Algorithms
*Brain/physiology
Male
Adult
Signal Processing, Computer-Assisted
Female

@article {pmid40594416,
year = {2025},
author = {Wechakarn, P and Klomchitcharoen, S and Jatupornpoonsub, T and Jirakittayakorn, N and Puttanawarut, C and Likitsuntonwong, W and Chaimongkolnukul, K and Wongsawat, Y},
title = {Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22166},
pmid = {40594416},
issn = {2045-2322},
mesh = {Animals ; *Brain Injuries, Traumatic/surgery/mortality ; *Stereotaxic Techniques ; Disease Models, Animal ; Rats ; *Neurosurgical Procedures/methods/instrumentation ; Male ; Operative Time ; Rats, Sprague-Dawley ; Mice ; },
abstract = {Controlled cortical impact (CCI) is the most widely used mechanical model of traumatic brain injury (TBI) in rodent brains. This neurosurgical procedure generally involves the use of a stereotaxic system, which requires reaching a specific brain region with the most accurate position possible. In this study, a modified stereotaxic system for TBI induction was developed to evaluate preclinical research in rodents for conducting neural stimulation experiments by using an implanted electrode to assist in rehabilitation after severe TBI. The proposed model aims to reduce animal mortality during surgery and alleviate the negative side effects potentially caused by prolonged anesthesia drug usage. Isoflurane is applied as an anesthetic drug before stereotaxic surgery in rodents, which promotes hypothermia in the animal body. The result showed notable improvement in rodent survival after applying an active warming pad system to prevent hypothermia. Compared with the conventional stereotaxic system, the modified CCI device with a mounted 3D-printed header significantly improved performance in the surgical procedure, decreasing the total operation time by 21.7%, especially in the Bregma‒Lambda measurement. These findings indicate the tangible capability of our modified stereotaxic system, which allows surgeons to perform stereotaxic surgery faster and lowers the risk of intraoperative mortality.},
}

Animals
*Brain Injuries, Traumatic/surgery/mortality
*Stereotaxic Techniques
Disease Models, Animal
Rats
*Neurosurgical Procedures/methods/instrumentation
Male
Operative Time
Rats, Sprague-Dawley
Mice

@article {pmid40594365,
year = {2025},
author = {Hadi-Saleh, Z and Mosleh, M and Al-Shahe, MA and Mosleh, M},
title = {Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21202},
pmid = {40594365},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Movement/physiology ; Brain/physiology ; },
abstract = {The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement intentions. This technology has many applications in the fields of medicine, rehabilitation, mind-controlled computers and assistive technologies. Despite significant progress in EEG-based BCI systems, there are challenges such as signal noise, low decoding accuracy, instability and changeability of signals, etc. To address these limitations, this article presents a new approach to classify MI from EEG signals with the help of synergistic Hilbert-Huang Transform(HHT) as pre-processing, Permutation Conditional Mutual Information Common Space Pattern (PCMICSP) as features and optimized back propagation neural network(BPNN) based on Honey Badger Algorithm(HBA) as classifier. Using the ergodicity of the HBA, along with chaotic mechanisms and global convergence, this approach encodes and optimizes the weights and thresholds of a BPNN. Initially, a comprehensive optimal solution is obtained through the honey badger algorithm. Subsequently, this solution is further refined to reach a more precise optimal state by introducing chaotic disturbances. The proposed method efficiency was confirmed through experimental analysis on a set of data of benchmark that is generally accessible of EEGMMIDB (imagery database or motor movement of EEG). Our experimental analysis outcome showed that mechanism development is important. Now, two EEG signal levels were taken into consideration: the first being an epileptic and the other being non-epileptic. The presented technique generated a max accuracy of 89.82% in comparison with other methods.},
}

*Electroencephalography/methods
Humans
*Algorithms
*Neural Networks, Computer
*Brain-Computer Interfaces
Signal Processing, Computer-Assisted
*Imagination/physiology
Movement/physiology
Brain/physiology

@article {pmid40590757,
year = {2025},
author = {Chang, T and Cho, SI and Chai, JY and Min, KD},
title = {Implications of predator species richness in terms of zoonotic spillover transmission of filovirus diseases in Africa.},
journal = {Transactions of the Royal Society of Tropical Medicine and Hygiene},
volume = {},
number = {},
pages = {},
doi = {10.1093/trstmh/traf065},
pmid = {40590757},
issn = {1878-3503},
support = {NRF-2021R1C1C2012611//National Research Foundation of Korea/ ; },
abstract = {BACKGROUND: A rich biodiversity of predators has been suggested to suppress the risk of zoonotic spillover by regulating prey abundance and behavior. We evaluated the association between predator species richness and spillover events of Ebolavirus and Marburgvirus in Africa.

METHODS: Historical records of filovirus outbreaks, along with ecological, geographical and socioeconomic factors, were considered in this environmental study. We used the maximum entropy approach (Maxent modeling) and stacked species distribution models to estimate predator species richness. Logistic regression analyses accounting for spatiotemporal autocorrelations were conducted to assess the association between predator species richness and spillover risk, adjusting for potential confounders.

RESULTS: Higher species richness of certain predators-the order Strigiformes and the family Colubridae-was associated with lower risks of Ebolavirus spillover, but not with Marburgvirus spillover. The third quartile (OR=0.02, 95% Bayesian credible interval [BCI]=0.00-0.84) and fourth quartile (OR=0.07, 95% BCI=0.00-0.42) of Strigiformes species richness, as well as the third quartile (OR=0.15, 95% BCI=0.01-0.73) and fourth quartile (OR=0.53, 95% BCI=0.03-0.85) of Colubridae species richness, were significantly associated with reduced odds of Ebolavirus index cases.

CONCLUSION: These findings support a possible role for predator species richness in suppressing zoonotic spillover.},
}

@article {pmid40590380,
year = {2025},
author = {Kushwaha, N and Mishra, N and Lalawat, RS and Padhy, PK and Gupta, VK},
title = {Automated posture adjustment system for immobilized patients using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2025.2523322},
pmid = {40590380},
issn = {1476-8259},
abstract = {This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.},
}

@article {pmid40590025,
year = {2025},
author = {Deuel, TA and Wenlock, J and McGovern, A and Rosenthal, J and Pampin, J},
title = {Musical auditory feedback BCI: clinical pilot study of the Encephalophone.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1592640},
pmid = {40590025},
issn = {1662-5161},
abstract = {INTRODUCTION: Therapeutic strategies for patients with severe acquired motor disability are relatively limited and show variable efficacy. Innovative technologies such as brain-computer interfaces (BCIs) have been developed recently that might benefit certain types of patients.

METHODS: Here, we tested a previously described auditory BCI, the Encephalophone, which may offer new options to improve quality of life and function. Eleven subjects with acquired moderate to severe motor disability, who had lost their ability to express themselves musically, were enrolled and 10 completed a clinical pilot study of the hands-free Encephalophone brain-computer interface (BCI). Subjects were briefly instructed on the use of the Encephalophone BCI, which uses EEG measured motor imagery to allow users to generate musical notes in real time without requiring movement. Subjects then underwent a pitch-matching task, a measure of accuracy, to attempt to match a given target pitch 3 times within 10 s. They were allowed free play, where they could improvise music over a backing track. After 2-3 songs - approximately 10 min - of freely improvised playing, subjects repeated the pitch-matching task. There were 3 sessions of testing and free play per subject, within 2 weeks, with at least 1 day separating sessions.

RESULTS: All subjects, on average, improved their pitch-matching accuracy by 15.6 percentage points and increased their number of hits by 58.7% over the 3 sessions, with all subjects scoring accuracy percentages significantly above random probability (19.05%). A subjective self-reporting survey of ratings of such factors as a feeling of expressing oneself, enjoyment, discomfort, and feeling of control showed a generally favorable response.

DISCUSSION: We suggest that this training approach using an auditory BCI may provide an innovative solution to challenges in recovery from motor disability.

CLINICAL TRIAL REGISTRATION: https://research.providence.org/clinical-research, Swedish Health Services #: STUDY2017000301.},
}

@article {pmid40588550,
year = {2025},
author = {Tian, Y and Li, H and Ye, W and Yuan, X and Guo, X and Guo, F},
title = {Temperature-dependent modulation of light-induced circadian responses in Drosophila melanogaster.},
journal = {The EMBO journal},
volume = {},
number = {},
pages = {},
pmid = {40588550},
issn = {1460-2075},
support = {32171008//the National Natural Science Foundation of China/ ; 32471210//the National Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2025ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//the Fundamental Research Funds for the Central Universities/ ; },
abstract = {Animals entrain their circadian rhythms to multiple external signals, such as light and temperature, which are integrated in master clock neurons to adjust circadian phases. However, the precise mechanisms underlying this process remain unclear. Here, we use in vivo two-photon calcium imaging while precisely controlling temperature to investigate how the Drosophila melanogaster circadian clock integrates light and temperature inputs in circadian neurons. We show that light responses modulate the circadian clock in central pacemaker neurons, with temperature acting as a fine-tuning mechanism to achieve optimal adaptation. Our results suggest that temperature-sensitive dorsal clock neurons DN1as regulate the light-induced firing of s-LNv circadian pacemaker neurons and release of the neuropeptide PDF through inhibitory glutamatergic signaling. Specifically, higher temperatures suppress s-LNv firing upon light exposure, while lower temperatures enhance this response. Behavioral analyses further indicate that lower temperatures accelerate phase adjustment, whereas higher temperatures decelerate them in response to new light-dark cycles. This novel mechanism of temperature-dependent modulation of circadian phase adjustment provides new insights into the adaptive strategies of animals for survival in fluctuating environments.},
}

@article {pmid40588517,
year = {2025},
author = {Ding, Y and Udompanyawit, C and Zhang, Y and He, B},
title = {EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5401},
pmid = {40588517},
issn = {2041-1723},
support = {NS124564, NS131069, NS127849, NS096761//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Robotics/methods/instrumentation ; *Fingers/physiology ; Male ; Adult ; Female ; *Hand/physiology ; Young Adult ; Movement/physiology ; Brain/physiology ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Robotics/methods/instrumentation
*Fingers/physiology
Male
Adult
Female
*Hand/physiology
Young Adult
Movement/physiology
Brain/physiology
Neural Networks, Computer
Imagination/physiology

@article {pmid40588007,
year = {2025},
author = {Mahoney, TB and Grayden, DB and John, SE},
title = {Sub-scalp EEG for sensorimotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade9f1},
pmid = {40588007},
issn = {1741-2552},
abstract = {To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.},
}

@article {pmid40587936,
year = {2025},
author = {Vadivelan D, S and Sethuramalingam, P},
title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110675},
doi = {10.1016/j.compbiomed.2025.110675},
pmid = {40587936},
issn = {1879-0534},
abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.},
}

@article {pmid40587626,
year = {2025},
author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X},
title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c06121},
pmid = {40587626},
issn = {1944-8252},
abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.},
}

@article {pmid40586134,
year = {2025},
author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C},
title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e03011},
doi = {10.1002/advs.202503011},
pmid = {40586134},
issn = {2198-3844},
support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; },
abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.},
}

@article {pmid40585760,
year = {2025},
author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G},
title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {3},
pages = {20240078},
pmid = {40585760},
issn = {2766-2098},
abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.},
}

@article {pmid40584823,
year = {2025},
author = {Jiang, M and Pan, X and Wang, X and Luo, Q},
title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1550536},
pmid = {40584823},
issn = {1662-5161},
abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.

METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).

RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.

DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.},
}

@article {pmid40584523,
year = {2025},
author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F},
title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1608645},
pmid = {40584523},
issn = {1664-2295},
abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.},
}

@article {pmid40584436,
year = {2025},
author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M},
title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {8},
pages = {1082-1099},
doi = {10.22038/ijbms.2025.85468.18477},
pmid = {40584436},
issn = {2008-3866},
abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.

MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.

RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.

CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.},
}

@article {pmid40584269,
year = {2025},
author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D},
title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {101},
doi = {10.1007/s11571-025-10279-1},
pmid = {40584269},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.},
}

@article {pmid40584164,
year = {2025},
author = {Sharma, MK and Chaudhary, S and Shenoy, S},
title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.},
journal = {MethodsX},
volume = {14},
number = {},
pages = {103382},
doi = {10.1016/j.mex.2025.103382},
pmid = {40584164},
issn = {2215-0161},
abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.},
}

@article {pmid40581689,
year = {2025},
author = {Olza, A and Soto, D and Santana, R},
title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {17},
pmid = {40581689},
issn = {2198-4018},
support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; },
abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.},
}