@article {pmid41060851,
year = {2025},
author = {Nguyen, MD and Do, T and Tran, XT and Nguyen, QT and Lin, CT},
title = {Edge AI-Brain-Computer Interfaces System: A Survey.},
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.2025.3618688},
pmid = {41060851},
issn = {1558-0210},
abstract = {Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.},
}

@article {pmid41060788,
year = {2025},
author = {Rosenthal, IA and Bashford, L and Bjanes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation: a case study of two participants.},
journal = {Journal of neurophysiology},
volume = {},
number = {},
pages = {},
doi = {10.1152/jn.00518.2024},
pmid = {41060788},
issn = {1522-1598},
support = {N/A//T&C Chen Brain-Interface Center/ ; N/A//James G. Boswell Foundation (Boswell Foundation)/ ; U01NS123127//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; T32 NS105595/NS/NINDS NIH HHS/United States ; },
abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously in a realistic visual condition compared to an abstract one, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts. This study was a part of a clinical trial (NCT01964261).},
}

@article {pmid41060749,
year = {2025},
author = {Ji, J and Luo, H and Su, J and Wang, S and Chen, X and Song, J},
title = {Multisensory electronic skin with decoupled pressure-temperature-sensing capabilities for similar object recognition.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {41},
pages = {e2519693122},
doi = {10.1073/pnas.2519693122},
pmid = {41060749},
issn = {1091-6490},
support = {2022YFC2401901//MOST | National Key Research and Development Program of China (NKPs)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation Prime Min//Prime Minister's Office Singapore (PMO)/ ; },
mesh = {Humans ; Pressure ; Touch/physiology ; Temperature ; Robotics ; Skin ; *Touch Perception/physiology ; *Wearable Electronic Devices ; *Thermosensing/physiology ; },
abstract = {Multisensory electronic skin (e-skin), which mimics the tactile capabilities of human skin, is pivotal in equipping robots with intelligent perceptual functions. Despite numerous advances in multifunctional perceptions, e-skin with combined mechano- and thermosensation capabilities for accurately recognizing objects with similar characteristics is still challenging. Here, we report a multisensory e-skin with a skin-like multilayer construction for smart perceptions, which features the patterned protrusion texture mimicking the skin texture to enhance the pressure-sensing sensitivity, the temperature-sensing component mimicking the thermoreceptors, the pressure-sensing component mimicking the mechanoreceptors, and the heater mimicking the body heat source. This multisensory e-skin exhibits excellent decoupled sensing performances of pressure and temperature, enabling the development of a haptic perception system for evaluating some discernible characteristics (e.g., shape and size) and experience-driven features (e.g., modulus and thermal conductivity) of objects through a simple grasp. Demonstrations of accurate recognition and automatic classification of various objects even with extremely similar surface features highlight the significant potential of this multisensory e-skin in applications such as intelligent soft robotics, prosthetics, and other related fields.},
}

Humans
Pressure
Touch/physiology
Temperature
Robotics
Skin
*Touch Perception/physiology
*Wearable Electronic Devices
*Thermosensing/physiology

@article {pmid41059626,
year = {2025},
author = {Meng, W and Hou, F and Chen, K and Ma, L and Liu, Q},
title = {Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550072},
doi = {10.1142/S0129065725500728},
pmid = {41059626},
issn = {1793-6462},
abstract = {Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.},
}

@article {pmid41059099,
year = {2025},
author = {Zhang, C and Liu, Y and Wu, X},
title = {TFANet: a temporal fusion attention neural network for motor imagery decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1635588},
pmid = {41059099},
issn = {1662-4548},
abstract = {INTRODUCTION: In the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.

METHODS: This paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.

RESULTS: The proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.

CONCLUSION: These results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.},
}

@article {pmid41058890,
year = {2025},
author = {Benachour, A and Medvedev, V and Zinchenko, O},
title = {Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1635677},
pmid = {41058890},
issn = {1664-1078},
abstract = {INTRODUCTION: Recent research has demonstrated the potential of utilizing mouse-tracking as a viable alternative method for examining attention-related attributes within the context of a multifaceted activity.

METHODS: In this study, a mouse-tracking technique was utilized to gather data from individuals who were involved in an online format of the Public Goods Game.

RESULTS: It was observed that participants exhibited distinct approaches to acquiring information while formulating decisions to propose high, moderate, or low offers. The mouse-tracking algorithm effectively distinguished between various types of offers made toward group funding, as evidenced by the measured distance of the cursor.

DISCUSSION: These findings suggest that mouse-tracking is a valuable tool for capturing decision-making processes and differentiating behavioral patterns in economic game contexts, offering insights into attention and choice mechanisms.},
}

@article {pmid41056741,
year = {2025},
author = {Yin, Y and Zhang, Y and Xu, S},
title = {The influence of money priming on conformity consumption: The distinct roles of self-sufficiency and self-control.},
journal = {Acta psychologica},
volume = {260},
number = {},
pages = {105682},
doi = {10.1016/j.actpsy.2025.105682},
pmid = {41056741},
issn = {1873-6297},
abstract = {Despite the pervasive role of money in society and the known psychological effects of money priming, research into its influence on consumer choices, especially regarding conformity behavior in consumption, remains limited. This study examines the impact of money priming on individual conformity behaviors within the context of Chinese consumption through three behavioral studies. Study 1 revealed that priming with money concepts reduces the tendency to conform. Study 2 investigated how feelings of monetary abundance and deprivation, elicited by money priming, affect conformity in consumption. The findings showed that a perceived sense of monetary abundance decreases conformity in consumption, whereas a sense of deprivation increases it. While product types did affect conformity consumption, they did not significantly interact with monetary primes. Study 3 explored the mediating roles of self-sufficiency and self-control, confirming that monetary abundance decreases conformity by enhancing self-sufficiency, and monetary deprivation increases conformity by diminishing self-control. These results suggest that money priming can trigger distinct feelings of abundance and deprivation, each having differential effects on conformity consumption. Understanding these effects can enable marketers to tailor strategies for personalized marketing or group purchasing initiatives, effectively addressing different market segments.},
}

@article {pmid41055454,
year = {2025},
author = {Xiang, Y and He, X and Cheng, T and Zhu, W and Pang, J and Cao, Y and Wu, M and Pei, R and Cao, Y},
title = {A Zwitterionic Conductive Hydrogel Interface for Enhanced Electrocorticography Signal Fidelity via High Conductivity, Antifouling, and Brain-Matched Mechanics.},
journal = {Biomacromolecules},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.biomac.5c01412},
pmid = {41055454},
issn = {1526-4602},
abstract = {Electrocorticography (ECoG) holds considerable promise for neural signal monitoring with high spatiotemporal resolution. However, conventional rigid ECoG electrodes are often hampered by poor mechanical compliance and insufficient resistance to biofouling, leading to high interfacial impedance and compromised signal quality. While integrating conductive hydrogels into ECoG interface offers a potential solution, concurrently achieving high conductivity, mechanical compatibility with brain tissue, biosafety, and robust antifouling remains a significant challenge. This study introduces SPP@NaCl, a novel zwitterionic conductive hydrogel synthesized by doping a poly(sulfobetaine methacrylate) (pSB) hydrogel matrix with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and employing NaCl as a Lewis acid to induce phase separation, thereby promoting an interconnected PEDOT network. The resultant SPP@NaCl hydrogel exhibits a compelling combination of properties: high electrical conductivity (∼9 S·m[-][1]), a low Young's modulus (1.74 kPa) that closely matches brain tissue, excellent conformability, and markedly reduced protein adsorption attributable to its zwitterionic structure. When integrated with commercial ECoG electrodes, the optimized SPP@NaCl-8 hydrogel dramatically lowers interfacial impedance. The resulting Au-SPP@NaCl electrodes enabled high-fidelity, real-time monitoring of cortical epileptiform discharges in a rat seizure model and demonstrated stable, long-term neural signal acquisition in anesthetized healthy rats. This work presents a new strategy for constructing ECoG interfaces that simultaneously deliver high conductivity, mechanical compliance, biosafety, and antifouling capabilities, highlighting the significant potential of these hydrogel-integrated ECoG electrodes for advanced brain-computer interface applications.},
}

@article {pmid41054887,
year = {2025},
author = {Ye, Y and Chen, S and Zhang, Y and Zhang, T and Liao, T and Ren, Z and Chen, W and Hu, W},
title = {Mechano-Locking Strategy for Broad-Spectrum SARS-CoV-2 Neutralization.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e05582},
doi = {10.1002/smll.202505582},
pmid = {41054887},
issn = {1613-6829},
support = {T2394511//National Science Foundation of China/ ; T2394510//National Science Foundation of China/ ; 92359303//National Science Foundation of China/ ; 92269101//National Science Foundation of China/ ; LY23A020002//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Viral entry into host cells is typically initiated by interactions between viral surface proteins and host cell receptors. Conventional neutralization strategies aim to disrupt these interactions but often lose effectiveness against rapidly mutating viral strains. This challenge extends beyond SARS-CoV-2 to other viruses such as HIV and influenza. To overcome this limitation, a novel mechano-locking strategy is proposed, using SARS-CoV-2 as a model system, in which bispecific antibodies (bsAbs) lock the spike protein in its prefusion conformation by preventing force-induced conformational changes. These bsAbs demonstrate broad-spectrum neutralization efficacy against multiple SARS-CoV-2 variants in pseudoviral assays. Single-molecule magnetic tweezers experiments further reveal that these bsAbs significantly raise the mechanical force threshold required for S1-S2 dissociation, thereby enhancing spike protein mechano-stability. This stabilization mechanism offers a mutation-resistant approach to neutralization and introduces a new design paradigm for antiviral therapeutics. These findings establish a mechanistically driven framework for developing biomechanically enhanced strategies potentially applicable to a wide range of mechanically activated enveloped viruses.},
}

@article {pmid41052978,
year = {2025},
author = {Liang, R and Fang, T and Wang, L and Ren, J and Meng, L and Zhao, M and Zheng, C and Fan, Q and Chen, Y and Yang, J and Ming, D},
title = {Multi-connectomics underpin emotional dysfunction in mouse exposed to simulated space composite environment.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {359},
pmid = {41052978},
issn = {2158-3188},
mesh = {Animals ; Mice ; *Connectome ; Male ; Magnetic Resonance Imaging ; *Prefrontal Cortex/diagnostic imaging/physiopathology ; *Emotions/physiology ; Mice, Inbred C57BL ; Space Flight ; Behavior, Animal/physiology ; Gray Matter/diagnostic imaging/pathology/physiopathology ; *Space Simulation ; *Brain/diagnostic imaging/physiopathology ; Nerve Net/physiopathology/diagnostic imaging ; },
abstract = {Long-duration space exploration, including missions to the Moon and Mars, demands strategies to preserve astronauts' emotional well-being for optimal performance. This study combines behavioral phenotyping, multimodal MRI, in vivo calcium imaging, and brain-wide genomics to bridge macroscopic brain function with mesoscopic neural activity and microscopic genetic processes, providing a dynamic characterization of the mouse connectome under simulated spaceflight conditions. We observed a reduction in gray matter volume, particularly in the prefrontal cortex, with prolonged exposure. Simulated space composite environment (SSCE) disrupted multi-scale functional connectivity and altered the macro-organizational functional gradient, reversing the relationship between brain function and emotional behaviors. Neural activity in the medial prefrontal cortex demonstrated exposure-time-dependent changes across emotional tasks, while genetic analyses linked SSCE-induced alterations in functional profiles to synaptic function and ion channel activity. Our findings reveal how extreme environments impact emotional behaviors, brain networks, and neural activity, offering insights for interventions to maintain brain integrity during extended space missions.},
}

Animals
Mice
*Connectome
Male
Magnetic Resonance Imaging
*Prefrontal Cortex/diagnostic imaging/physiopathology
*Emotions/physiology
Mice, Inbred C57BL
Space Flight
Behavior, Animal/physiology
Gray Matter/diagnostic imaging/pathology/physiopathology
*Space Simulation
*Brain/diagnostic imaging/physiopathology
Nerve Net/physiopathology/diagnostic imaging

@article {pmid41052270,
year = {2025},
author = {Lu, Y and Xiong, T and Liu, Y and Zhou, H and Xie, B and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Gate Capacitance-Dependent Neuromorphic Functions of Organic Electrochemical Transistors.},
journal = {The journal of physical chemistry letters},
volume = {},
number = {},
pages = {10678-10684},
doi = {10.1021/acs.jpclett.5c02510},
pmid = {41052270},
issn = {1948-7185},
abstract = {Neuromorphic functions of organic electrochemical transistors (OECTs) have attracted enormous research attention due to their promising application in the field of brain-mimicking computing and brain-computer interfaces. However, the essential role of gate electrodes in the neuromorphic functions of these synaptic transistors remains unclear. Herein, we systematically investigated the influence of gate electrodes on the neuromorphic functions of synaptic OECTs by rationally choosing four kinds of typical gate electrodes: bare glass carbon electrode (Bare-GCE), carbon nanotube-modified GCE (CNT-GCE), PEDOT:PSS modified GCE (PEDOT:PSS-GCE), and Ag/AgCl electrode. Evaluations of the neuromorphic functions indicated that gate capacitance controlled the performance of synaptic OECTs by tuning the electrical field distribution and doping kinetics in the ionic circuits. This systematic exploration of the gate electrode influences on the OECTs offers rational guidance for the structural design of synaptic OECTs.},
}

@article {pmid41052170,
year = {2025},
author = {Zhang, M and Zhao, S and Xie, L and Liu, T and Yao, D and Yin, E},
title = {Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3613730},
pmid = {41052170},
issn = {1558-2531},
abstract = {Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition systems is hindered by low signal-to-noise ratios and the scarcity of labeled data. While existing studies often tackle these challenges in isolation, we propose a novel Cross-Device Representation Consistency (CDRC) pretraining paradigm that addresses both issues simultaneously. CDRC leverages self-supervised signals derived from representation distances and is trained through contrastive estimation. Specifically, our approach employs a transformer based dual-branch single-view embedding prediction task, combining with a contrastive feature alignment module to extract robust and discriminative representations. We first evaluate the CDRC model on a low signal-to-noise ratio emotion classification task involving wearable dry electrodes. Furthermore, we extend CDRC to a multimodal fusion setting to address a cross-device vigilance regression task involving heterogeneous physiological modalities. Extensive experiments on the PaDWEED and SEED-VIG datasets demonstrate that CDRC achieves performance comparable to fully supervised methods and reaches the stat-of-the-art results of existing self-supervised methods, setting a new benchmark in this field. Notably, its strong performance on subject-independent tasks highlights its effectiveness in mitigating subject variability. These results underscore the potential of CDRC to significantly enhance the practicality and scalability of EEG-based recognition systems, marking a meaningful step toward real-world brain-computer interfaces.},
}

@article {pmid41044400,
year = {2025},
author = {Serafini, ERDS and Guerrero-Mendez, CD and Blanco-Diaz, CF and da Silva Fiorin, F and de Albuquerque, TS and A Dantas, AFO and Delisle-Rodriguez, D and do Espírito-Santo, CC},
title = {Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34633},
pmid = {41044400},
issn = {2045-2322},
mesh = {Humans ; *Spinal Cord Injuries/physiopathology/rehabilitation/complications ; Male ; *Brain-Computer Interfaces ; Adult ; *Gait/physiology ; Middle Aged ; *Robotics/methods ; *Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy ; *Urinary Bladder/physiopathology ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Neurofeedback ; },
abstract = {Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this dysfunction. This study proposes robotic-assisted gait training combined with motor imagery (MI)-based brain-computer interface (BCI) to induce improved cortical modulation, and consequently improve bladder function in patients with SCI. The study involved seven men with complete and chronic SCI in a protocol comprising 24 sessions of robotic-assisted walking with BCI and MI. This regimen was designed to teach both mu (µ, 8-12 Hz) and beta (β, 15-20 Hz) modulation through MI practices using multi-channel EEG neurofeedback (NFB), focusing on sensorimotor rhythm (SMR) activation. Clinical outcomes were measured using the neurogenic bladder symptom score (NBSS), which revealed substantial improvements in bladder control among participants. EEG analysis confirmed a significant correlation between modulation of µ and β rhythms with decreased NBSS scores. Our findings support that robotic-assisted gait training combined with MI-based BCI effectively modulates with more precision the cortical µ and β rhythms and improves NB dysfunction in SCI individuals.},
}

Humans
*Spinal Cord Injuries/physiopathology/rehabilitation/complications
Male
*Brain-Computer Interfaces
Adult
*Gait/physiology
Middle Aged
*Robotics/methods
*Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy
*Urinary Bladder/physiopathology
Electroencephalography
*Imagery, Psychotherapy/methods
Neurofeedback

@article {pmid41044308,
year = {2025},
author = {Chen, Z and Cao, Y and Fu, Q and Hou, L},
title = {Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34555},
pmid = {41044308},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; Electroencephalography/methods ; *Attention/physiology ; Male ; Adult ; *Brain/physiology ; Female ; *Imagination/physiology ; },
abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal decoding. This study presents a systematic investigation of hierarchical deep learning architectures for motor imagery classification, introducing a novel attention-enhanced convolutional-recurrent framework that achieves state-of-the-art accuracy of 97.2477% on a custom four-class motor imagery dataset comprising 4,320 trials from 15 participants. By synergistically integrating spatial feature extraction through convolutional layers, temporal dynamics modeling via long short-term memory networks, and selective attention mechanisms for adaptive feature weighting, our approach significantly outperforms conventional methods while providing interpretable insights into the spatiotemporal signatures of motor imagery. Beyond demonstrating competitive performance, this work elucidates the critical role of attention mechanisms in capturing task-relevant neural patterns amidst the high-dimensional, non-stationary nature of EEG signals. Our findings demonstrate that biomimetic computational architectures that mirror the brain's own selective processing strategies can substantially enhance BCI reliability, offering immediate implications for neurorehabilitation technologies and broader applications in restorative neuroscience. Our code is available at https://github.com/Laboratory-EverythingAI/-EEG_Classification .},
}

*Brain-Computer Interfaces
Humans
*Deep Learning
Electroencephalography/methods
*Attention/physiology
Male
Adult
*Brain/physiology
Female
*Imagination/physiology

@article {pmid41043460,
year = {2025},
author = {Rasheed, S and Bennett, J and Yoo, PE and Burkitt, AN and Grayden, DB},
title = {Decoding saccadic eye movements from brain signals using an endovascular neural interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0f52},
pmid = {41043460},
issn = {1741-2552},
abstract = {An Oculomotor Brain-Computer Interface (BCI) records neural activity from brain regions involved in planning eye movements and translates this activity into control commands. While previous successful studies have relied on invasive implants in non-human primates or electrooculography (EOG) artefacts in human electroencephalogram (EEG) data, this study aimed to demonstrate the feasibility of an oculomotor BCI using a minimally invasive endovascular StentrodeTM device implanted near the supplementary motor area of a patient with Amyotrophic Lateral Sclerosis (ALS). Approach. One participant performed self-paced visually-guided and free-viewing saccade tasks in four directions (left, right, up, down) while endovascular EEG and eye gaze recordings were collected. Visually-guided saccades were cued with visual stimuli, whereas free-viewing saccades were self-directed without explicit cues. Brain signals were pre-processed to remove cardiac artefacts, downsampled, and classified using a Random Forest algorithm. For saccade onset classification (fixation vs. saccade), features in time and frequency domains were extracted after xDAWN denoising, while for saccade direction classification, the downsampled time series were classified directly without explicit feature extraction. Main results. The neural responses of visually-guided saccades overlapped with cue-evoked potentials, while free-viewing saccades exhibited saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Saccade onset classification was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 across sessions. Saccade direction decoding yielded within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best performing binary comparisons (left vs. up and left vs. down). Significance. This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in a patient with ALS, establishing a foundation for future oculomotor BCI studies in human subjects.},
}

@article {pmid41042834,
year = {2025},
author = {Guo, M and Zhang, J and Liu, H and Bai, Y and Ni, G},
title = {Signal-to-Noise Ratio Effects Frontoparietal Network Lateralization: Electroencephalogram Evidence in Underwater Auditory Target Recognition.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70081},
pmid = {41042834},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
abstract = {Accurately recognizing auditory targets within background interference remains challenging at a low signal-to-noise ratio (SNR). Using an oddball paradigm, this electroencephalogram study investigated the impact of SNR (0, -10, and -20 dB) on psychophysiological processes underlying underwater auditory target recognition in twenty normal-hearing participants. Reduced SNR impaired the N1-P2 component and led to P300 variations, with delayed latencies (N1: p = 0.0355; P300: p = 0.0075) and reduced amplitudes (P2: p = 0.0075; P300: p = 0.0277), indicating increased attentional demands. Microstate analysis highlighted 300-400 ms frontoparietal activation for attention orientation and sensory information integration. Reduced accuracy correlates with alpha-band activity and phase variations over frontoparietal areas (event-related spectral perturbation [ERSP]: p = 0.0388; inter-trial coherence [ITC]: p = 0.0059), implying suppression of task-relevant processing. Gamma-band activity and phase at lower SNR levels suggest changes in the parietal network's function (ERSP: p = 0.0183; ITC: p = 0.0113), influencing reaction times due to increased integration difficulty. Right-lateralized alpha- and gamma-band network shifts support the functional advantages of the right hemisphere in noise, with enhanced local efficiency (frontal alpha: p = 0.0100; parietal-occipital gamma: p = 0.0116). These findings provide insights into the psychophysiological mechanisms underlying auditory target recognition in noise.},
}

@article {pmid41042451,
year = {2025},
author = {Huang, Y and Ke, Y and Li, J and Liu, S and Ming, D},
title = {Frontal Theta Modulation in Sequential Working Memory: the Impact of Spatial Regularity and Scenario.},
journal = {Brain topography},
volume = {38},
number = {6},
pages = {74},
pmid = {41042451},
issn = {1573-6792},
support = {No. 2021YFF1200603//the National Key Research and Development Program of China/ ; No. 62276184 and 61806141//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Memory, Short-Term/physiology ; *Theta Rhythm/physiology ; Male ; Female ; Young Adult ; Adult ; Electroencephalography ; *Frontal Lobe/physiology ; *Space Perception/physiology ; },
abstract = {Humans can quickly extract spatial regularities from sequences to reduce working memory (WM) load, yet the electrophysiological mechanisms remain unclear. Although previous studies have underscored the role of frontal-midline theta (FM-theta) in sequential WM processing, whether and how spatial regularity modulates FM-theta is unknown. To investigate this, we varied the spatial relation between successive items-more repetitions of the same displacement yielded fewer unique chunks and thus higher regularity-while sequence length stayed fixed. Participants were asked to encode, maintain and reproduce the temporal order of sequences utilizing their spatial structures. To enhance ecological validity, we further embedded the task in a complex scenario that included meaningful contexts, dispersed layouts, and variable stimulus sizes. Behavioral data revealed that sequences with higher regularity and the simple scenario yielded higher accuracy, confirming successful manipulations of regularity and scenario difficulty. The overall temporal dynamics of EEG data showed prominent theta enhancement and concurrent alpha/beta suppression during encoding and maintenance. Subsequent analyses across the 4-30 Hz and delay period demonstrated that theta power increased while alpha/beta power declined monotonically with sequence complexity. Notably, regularity-modulated alpha power differed in two scenarios. Moreover, the results found that only sequence regularity-not scenario difficulty-modulated fronto-posterior theta connectivity and slowed the FM-theta frequency. In sum, FM-theta, operating through long-range connectivity and frequency modulation, exclusively tracks spatial-regularity demands in sequential WM, while such neural mechanisms remain impervious to variations in scenario difficulty. These findings suggest that FM-theta may serve as a specific neural marker for spatial regularity processing, rather than a general index of task difficulty, thereby offering a concrete target for future neuromodulatory interventions.},
}

Humans
*Memory, Short-Term/physiology
*Theta Rhythm/physiology
Male
Female
Young Adult
Adult
Electroencephalography
*Frontal Lobe/physiology
*Space Perception/physiology

@article {pmid41042091,
year = {2025},
author = {Sato, K and Tanaka, R and Ota, K},
title = {BCI-Mediated Warfare, Psychological Distance, and the Duty to Care.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {344-346},
doi = {10.1080/21507740.2025.2557822},
pmid = {41042091},
issn = {2150-7759},
}

@article {pmid41040967,
year = {2025},
author = {Wood, C and Wang, H and Yang, WJ and Xi, Y},
title = {Facing the possibility of consciousness in human brain organoids.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {9},
pages = {101365},
pmid = {41040967},
issn = {2666-3899},
abstract = {Human brain organoids (HBOs) have emerged as transformative models for neurodevelopment and disease, yet ethical concerns persist regarding their potential to develop consciousness. Since 2020, a growing cohort of neuroscientists and philosophers has dismissed these concerns as unscientific, citing limited structural complexity, absence of bodily integration and environmental interaction, and a prevailing neuroscientific consensus against the feasibility of any, or any near-future, emergence of HBO consciousness, thus challenging any suggested revisions of ethical guidelines and safeguards. We argue that this dismissal is premature. Drawing on neuroscientific benchmarks, comparisons to the developing human brain, contemporary theories of consciousness, and principles of natural developmental progression, we question the basis for selectively excluding consciousness from among HBOs' expanding functional repertoire. We caution against enshrining such skepticism into dogma or using it to defer ethical engagement. Instead, we advocate for proactive, ongoing assessment of the moral implications of advancing HBO capabilities.},
}

@article {pmid41040697,
year = {2025},
author = {Chetty, N and Kacker, K and Feldman, AK and Yoo, PE and Bennett, J and Fry, A and Tal, I and Hardy, NF and Ebrahimi, S and Echavarria, C and Sawyer, A and Schone, HR and Harel, NY and Nogueira, RG and Majidi, S and Levy, EI and Kandel, A and Hill, KK and Opie, NL and Lacomis, D and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ},
title = {Signal properties and stability of a chronically implanted endovascular brain computer interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.19.25335897},
pmid = {41040697},
abstract = {BACKGROUND: Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.

METHODS: We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.

RESULTS: Motor-related modulation in neural activity was exhibited in the high-frequency bands (30-200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.

CONCLUSIONS: These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.},
}

@article {pmid41040692,
year = {2025},
author = {Schone, HR and Yoo, P and Fry, A and Chetty, N and Sawyer, A and Herbers, C and Liu, F and Moon, CH and Hill, K and Majidi, S and Harel, NY and Nogueira, RG and Levy, E and Putrino, DF and Lacomis, D and Oxley, TJ and Weber, DJ and Collinger, JL},
title = {Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.19.25335875},
pmid = {41040692},
abstract = {Brain-computer interfaces (BCIs) are an emerging assistive technology for individuals with motor impairments, enabling the command of digital devices using neural signals. The Stentrode BCI is an implant, positioned within the brain's neurovasculature, that can record movement-related electrocortical activity. Over 5 years, 10 participants (8 amyotrophic lateral sclerosis, 1 primary lateral sclerosis, 1 brainstem stroke) have been implanted with a Stentrode BCI and significant inter-participant variability has been observed in the recorded motor signal strength. This variability warrants a critical investigation to characterize potential predictors of signal strength to promote more successful BCI control in future participants. Therefore, we investigated the relationship between Stentrode BCI motor signal strength and a variety of user-specific factors: (1) clinical status, (2) pre-implant functional activity, (3) peri-implant neuroanatomy, (4) peri-implant neurovasculature, and (5) Stentrode device integrity. Data from 10 implanted participants, including clinical demographics, pre- and post-implant neuroimaging and longitudinal Stentrode BCI motor signal assessments were acquired over a year. Across all potential predictors, the strongest predictor of Stentrode motor signal strength was the degree to which the Stentrode BCI's deployment position overlapped with primary motor cortex (M1). These findings highlight the importance of targeting M1 during device deployment and, more generally, provides a scientific framework for investigating the role of user-specific factors on BCI device outcomes.},
}

@article {pmid41040179,
year = {2025},
author = {Rigotti-Thompson, M and Nason-Tomaszewski, SR and Bechefsky, P and Acosta, A and Hahn, N and Avansino, D and Richards, B and Nicolas, C and Ali, YH and Henderson, JM and Hochberg, LR and AuYong, N and Pandarinath, C},
title = {Preparatory encoding of diverse features of intended movement in the human motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.24.678356},
pmid = {41040179},
issn = {2692-8205},
abstract = {Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.},
}

@article {pmid41039114,
year = {2025},
author = {},
title = {High-resolution brain-computer interface with electrode scalability and minimally invasive surgery.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039114},
issn = {2157-846X},
}

@article {pmid41039113,
year = {2025},
author = {Hettick, M and Ho, E and Poole, AJ and Monge, M and Papageorgiou, D and Takahashi, K and LaMarca, M and Trietsch, D and Reed, K and Murphy, M and Rider, S and Gelman, KR and Byun, YW and Miller, JS and Hanson, T and Tolosa, V and Lee, SH and Bhatia, S and Konrad, PE and Mager, M and Mermel, CH and Rapoport, BI},
title = {Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039113},
issn = {2157-846X},
abstract = {High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.},
}

@article {pmid41039090,
year = {2025},
author = {Zhou, H and Wang, M and Qi, S and Chen, Q and Lai, J and Wu, Z and Liu, R and Wang, L and Zhou, H and Zhang, S and Hu, S},
title = {Transcranial temporal interference stimulation for treating bipolar disorder with depressive episodes: a feasibility Study.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41039090},
issn = {1476-5578},
support = {52407261//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Bipolar depression (BD-D) is a significant clinical challenge associated with high disease burden. Transcranial temporal interference stimulation (tTIS), a novel and noninvasive approach for targeting deep brain structures, was investigated for its efficacy and safety in BD-D patients in this trial. Thirty-six patients were recruited for a single-arm, open-label trial, and 25 completed the 5-day intervention consisting of 10 tTIS sessions targeting the left nucleus accumbens. Each session lasted 20 min, with a maximum current intensity of 2 mA and an envelope stimulation frequency of 40 Hz. Significant symptom reductions were observed following treatment, with mean HAMD-17 scores decreasing from 23.36 to 16.16 (p < 0.0001), MADRS scores from 39.12 to 31.28 (p < 0.01), HAMA scores from 19.68 to 15.44 (p < 0.05), and QIDS scores from 13.52to 9.68 (p < 0.001). Eleven participants (44.0%) met improvement criteria and seven (28.0%) achieved response. Cognitive assessments indicated improvements in memory and executive function, and changes in reward-related brain activity correlated positively with symptom reduction. Adverse events were mild, mainly transient scalp discomfort. These findings provide preliminary evidence supporting the efficacy and safety of tTIS for alleviating depressive symptoms and cognitive impairments in BD-D.},
}

@article {pmid40788303,
year = {2025},
author = {Shotbolt, M and Bryant, J and Liang, P and Khizroev, S},
title = {Mechanism and applications of magnetoelectric nanoparticles in cancer therapy.},
journal = {Nanomedicine (London, England)},
volume = {20},
number = {19},
pages = {2469-2481},
pmid = {40788303},
issn = {1748-6963},
mesh = {Humans ; *Neoplasms/drug therapy/therapy ; *Nanoparticles/chemistry/therapeutic use ; Animals ; Drug Delivery Systems/methods ; Nanomedicine/methods ; Magnetic Fields ; *Antineoplastic Agents/therapeutic use/administration & dosage ; },
abstract = {Cancer remains a major clinical challenge, with current therapies often hampered by off-target effects, drug resistance, and incomplete tumor eradication. There is a pressing need for more precise and effective treatment strategies. This review explores the mechanisms and applications of magnetoelectric nanoparticles (MENPs) in cancer therapy. MENPs, typically composed of magnetostrictive and piezoelectric materials in a core-shell structure, generate electric fields in response to magnetic fields, enabling targeted and noninvasive therapeutic actions. The literature search included recent advances in MENP synthesis, optimization of material composition and morphology, and preclinical studies demonstrating their ability to enhance drug delivery, disrupt tumor cell membranes, and induce tumor regression without systemic toxicity. Relevant studies were identified by searching electronic databases, including PubMed, Web of Science, Scopus, and Google Scholar. The search employed a combination of keywords and phrases such as "magnetoelectric nanoparticles," "MENPs," "cancer therapy," "nanomedicine," "core-shell nanoparticles," "magnetostrictive," "piezoelectric," "drug delivery," "magnetic field," "nano-electroporation," and "reactive oxygen species.." MENPs represent a promising option for precision oncology, offering remote control over therapeutic effects and the potential to overcome limitations of conventional treatments. Ongoing research should focus on optimizing MENP design for selectivity and efficacy, as well as advancing their clinical translation for cancer therapy.},
}

Humans
*Neoplasms/drug therapy/therapy
*Nanoparticles/chemistry/therapeutic use
Animals
Drug Delivery Systems/methods
Nanomedicine/methods
Magnetic Fields
*Antineoplastic Agents/therapeutic use/administration & dosage

@article {pmid41038246,
year = {2025},
author = {Xie, H and Xu, H and Xu, K and Yu, C and Yang, W and Yang, C},
title = {Rat Robot Autonomous Border Detection Based on Wearable Sensors.},
journal = {Bioinspiration & biomimetics},
volume = {},
number = {},
pages = {},
doi = {10.1088/1748-3190/ae0ee8},
pmid = {41038246},
issn = {1748-3190},
abstract = {Bio-robots, a novel type of robots created based on brain-machine interface, have shown great potential in search and rescue tasks. However, current research focuses on the bio-robot itself, such as locomotion, localization and navigation, but lacks interactions with the external environment. In this paper, we proposed a new system for rat robot to autonomously explore the border of unknown field out of sight, and then get the boundary map. We invented a wearable backpack, which is an embedded system with laser-ranging sensors, IMU and ultra-wide band (UWB) module, for the rat robot. Based on the wearable system, a classification method for motion states based on random forest (RF) and a navigation algorithm based on finite state machine (FSM) were developed for the autonomous exploration of border and tested in the locomotion experiment. Besides, with the localization and distance data from UWB and laser-ranging sensors, we mapped the distribution of the border, using Ramber-Douglas-Peucker (RDP) algorithm. The results show that the system could effectively navigate the rat robot to explore the field and accurately detect the border. The accuracy of classification reaches 97.86% and the error rate of border detection is 5.90%. This work provides a novel technology that has potential for practical applications such as prospect for minerals and search tasks. .},
}

@article {pmid41038061,
year = {2025},
author = {Wang, X and Li, X and Li, J and Fu, Y and Zhang, D and Peng, Y},
title = {RimeSleepNet: A hybrid deep learning network for s-EEG sleep stage classification.},
journal = {Sleep medicine},
volume = {136},
number = {},
pages = {106835},
doi = {10.1016/j.sleep.2025.106835},
pmid = {41038061},
issn = {1878-5506},
abstract = {Sleep stage classification is essential for sleep research and clinical diagnostics. However, frequency aliasing in sleep electroencephalogram (s-EEG) signals remains a significant challenge, existing methods have yet to effectively address this issue. This study proposes a hybrid deep-learning model, RimeSleepNet, comprising four key components. First, the rime optimization algorithm adaptively tunes variational mode decomposition (VMD) to reduce frequency aliasing by generating intrinsic mode functions (IMFs). Second, a convolutional neural network (CNN) automatically extracts stage-specific features from IMFs. A multi-head self-attention (MHSA) mechanism then dynamically weights these features to prioritize stage-specific patterns, followed by long short-term memory (LSTM) networks that model temporal dynamics for robust classification of NREM, REM, and WAKE stages. Evaluated on the Chengdu People's Hospital and Sleep-EDF datasets, RimeSleepNet achieves the highest F1 scores of 0.94, 0.89, and 0.92 for NREM, REM, and WAKE stages, respectively, with an AUC of 0.92, outperforming baseline models like CNN and LSTM. Cross-dataset validation confirms its robust generalization (Cohen's κ = 0.90), and it reduces validation loss by 53 % compared to LSTM, providing an advanced tool for automated sleep stage analysis in sleep disorder diagnosis and personalized monitoring.},
}

@article {pmid41036535,
year = {2025},
author = {Arif, S and Rehman, MZU and Mushtaq, Z},
title = {Editorial: Advancements in smart diagnostics for understanding neurological behaviors and biosensing applications.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1693327},
doi = {10.3389/fncom.2025.1693327},
pmid = {41036535},
issn = {1662-5188},
}

@article {pmid41035957,
year = {2025},
author = {Wang, W and Liu, Y and Shi, P and Zhang, J and Wang, G and Li, Y and Liu, W and Ming, D},
title = {Altered tactile abnormalities in children with ASD during tactile processing and recognition revealed by dynamic EEG features.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1611438},
pmid = {41035957},
issn = {1664-0640},
abstract = {INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by sensory processing abnormalities, particularly in tactile perception, highlighting the need for objective screening methods beyond current subjective behavioral assessments.

METHODS: This study developed a portable electro-tactile stimulation system with EEG to evaluate tactile processing differences in children with ASD (n=36) versus typically developing controls (n=36).

RESULTS: Revealing significantly reduced ERP amplitudes at key processing stages: P200 at FP2 (F(1,70)=10.82, p=0.0454), N200 at F3 (F(1,70)=58.33, p<0.0001), and P300 at C4 (F(1,70)=45.62, p<0.0001). Topographic analysis identified pronounced group differences (>10ìV) across frontal, central, and parietal regions (F8, FC5/6, CP1/2/5/6, Pz, Oz), with ASD children exhibiting prolonged but less efficient tactile discrimination and compensatory prefrontal activation (FP2 CV: p=0.043). The paradigm demonstrated strong reliability (CV ICC: ASD=0.779, TD=0.729) and achieved 85.2% classification accuracy (AUC=0.91) using ANN, with optimal performance from F8 P300 features (sensitivity=87.5%, specificity=83.7%).

DISCUSSION: These findings provide an objective, efficient (15-minute) screening method that advances understanding of tactile processing abnormalities in ASD and supports the development of physiological biomarkers for early identification, overcoming limitations of questionnaire-based approaches.},
}

@article {pmid41035905,
year = {2025},
author = {Xue, Y and Chen, Y and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y},
title = {Applications and interrelationships of brain function detection, brain-computer interfaces, and brain stimulation: a comprehensive review.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {161},
pmid = {41035905},
issn = {1871-4080},
abstract = {Brain-Computer Interface (BCI), Brain Function Detection (BFD), and Brain Stimulation (BS) are three pivotal technological domains in neuroscience and neuroengineering. Each plays a critical role in fundamental research, clinical applications, and human-computer interaction paradigms. Despite their distinct developmental pathways and application focuses, these technologies are frequently conflated or ambiguously referenced in both academic discourse and industrial practice, potentially leading to conceptual misinterpretations, suboptimal system designs, and clinical misapplications. Prior literature reviews have predominantly concentrated on BCI as a standalone subject, covering its historical evolution, specific neurophysiological signal modalities, or emergent technological trends. This manuscript's core contribution is critiquing the overuse of "passive BCI" (labeling feedback-absent monitoring as BCI). Through an application-oriented lens, it clarifies boundaries between BCI, BFD, and BS to resolve conceptual confusion. Further, the review interrogates the convergences and divergences among these modalities and critically evaluates the practical feasibility and challenges associated with their integrative deployment in clinical and experimental settings. Ultimately, this work aspires to provide a lucid, systematic, and conceptually coherent framework to support neuroscientific novices, interdisciplinary investigators, and clinical practitioners. By fostering precise comprehension and judicious utilization of BCI, BFD, and BS, it aims to propel their standardized advancement and enhance their translational impact across both research and clinical domains.},
}

@article {pmid41034549,
year = {2025},
author = {Di, S and Luo, N and Shi, W and Yang, Z and Sui, J and Jiang, R and Cui, Y and Du, Z and Zhang, J and Ma, Y and Wang, H and Chu, C and Zhong, Y and Li, W and Lu, Y and Yan, H and Liao, J and Zhang, D and Calhoun, V and Song, M and Jiang, T},
title = {Physical Activity and Depressive Mood Share the Structural Connectivity Between Motor and Reward Networks.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41034549},
issn = {1995-8218},
abstract = {In various studies, exercise has been revealed to have a positive effect on alleviating depressive symptoms. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism. In this study, we used a large neuroimaging cohort [n = 1,027, major depressive disorder (MDD)/healthy controls (HCs) = 492/535] from the UK Biobank to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as the biological interpretation. An SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discrimination between MDDs and HCs, primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and the astrocyte cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset (n = 3,496) and present commonalities with bipolar disorder (n = 81). Overall, these findings not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhance our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.},
}

@article {pmid41034219,
year = {2025},
author = {Griggs, WS and Norman, SL and Tanter, M and Liu, C and Christopoulos, V and Shapiro, MG and Andersen, RA},
title = {Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8752},
pmid = {41034219},
issn = {2041-1723},
support = {F30EY032799//U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)/ ; T32GM008042//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; R01NS123663//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Animals ; *Saccades/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Macaca mulatta ; Ultrasonography/methods ; *Functional Neuroimaging/methods ; Brain Mapping/methods ; Magnetic Resonance Imaging ; },
abstract = {The lateral intraparietal cortex (LIP), contained within the posterior parietal cortex (PPC), is crucial for transforming spatial information into saccadic eye movements, yet its functional organization for movement direction remains unclear. Here, we used functional ultrasound imaging (fUSI), a technique with high sensitivity, large spatial coverage, and good spatial resolution, to map movement direction encoding across the PPC by recording local changes in cerebral blood volume within PPC as two male monkeys performed memory-guided saccades. Our analysis revealed a heterogeneous organization where small patches of neighboring LIP cortex encoded different directions. These subregions demonstrated consistent tuning across several months to years. A rough topography emerged where anterior LIP represented more contralateral downward movements and posterior LIP represented more contralateral upward movements. These results address two fundamental gaps in our understanding of LIP's functional organization: the neighborhood organization of patches and the stability of these populations across long periods of time. By tracking LIP populations over extended periods, we developed mesoscopic maps of direction specificity previously unattainable with fMRI or electrophysiology methods.},
}

Animals
*Saccades/physiology
*Parietal Lobe/physiology/diagnostic imaging
Male
Macaca mulatta
Ultrasonography/methods
*Functional Neuroimaging/methods
Brain Mapping/methods
Magnetic Resonance Imaging

@article {pmid41034198,
year = {2025},
author = {Singh, A and Thomas, T and Li, J and Hickok, G and Pitkow, X and Tandon, N},
title = {Transfer learning via distributed brain recordings enables reliable speech decoding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8749},
pmid = {41034198},
issn = {2041-1723},
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Learning ; },
abstract = {Speech brain-computer interfaces (BCIs) combine neural recordings with large language models to achieve real-time intelligible speech. However, these decoders rely on dense, intact cortical coverage and are challenging to scale across individuals with heterogeneous brain organization. To derive scalable transfer learning strategies for neural speech decoding, we used minimally invasive stereo-electroencephalography recordings in a large cohort performing a demanding speech motor task. A sequence-to-sequence model enabled decoding of variable-length phonemic sequences prior to and during articulation. This enabled development of a cross-subject transfer learning framework to isolate shared latent manifolds while enabling individual model initialization. The group-derived decoder significantly outperformed models trained on individual data alone, enabling decoding robustness despite variable coverage and activation. These results highlight a pathway toward generalizable neural prostheses for speech and language disorders by leveraging large-scale intracranial datasets with distributed spatial sampling and shared task demands.},
}

Humans
*Brain-Computer Interfaces
*Speech/physiology
*Brain/physiology
Electroencephalography/methods
Male
Female
Adult
Young Adult
Middle Aged
Learning

@article {pmid41033466,
year = {2025},
author = {Dong, Z and Xiang, Y and Wang, S},
title = {High - Quality Decoding of RGB Images from the Neuronal Signals of the Pigeon Optic Tectum.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110595},
doi = {10.1016/j.jneumeth.2025.110595},
pmid = {41033466},
issn = {1872-678X},
abstract = {BACKGROUND: Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.

NEW METHOD: We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.

RESULTS: Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.

In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65%, the structural similarity index (SSIM) increased by 38.92%, the peak signal-to-noise ratio (PSNR) increased by 12.65%, and the feature similarity index (FSIMc) increased by 9.28%.

CONCLUSIONS: This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.},
}

@article {pmid41033328,
year = {2025},
author = {Deng, X and Fan, Z and Dong, W},
title = {MEFD dataset and GCSFormer model : Cross-subject emotion recognition based on multimodal physiological signals.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae0e28},
pmid = {41033328},
issn = {2057-1976},
abstract = {Cross-subject emotion recognition is an important research direction in the fields of affective computing and brain-computer interfaces, aiming to identify the emotional states of different individuals through physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). Currently, most EEG-based emotion recognition datasets are unimodal or bimodal, which may overlook the emotional information reflected by other physiological signals of the subjects. In this paper, a multimodal dataset named Multimodal Emotion Four Category Dataset (MEFD) is constructed, which includes EEG, Heart Rate Variability (HRV), Electrooculogram (EOG), and Electrodermal Activity (EDA) data from 34 participants in four emotional states: sadness, happiness, fear, and calm. This will contribute to the development of multimodal emotion recognition research. To address the recognition difficulty caused by individual differences in cross-subject emotion recognition tasks, a classification model named Global Convolution Shifted Window Transformer (GCSFormer) composed of an EEG-Swin Convolution module and an improved Global Adaptive Transformer (GAT) module is proposed. By using a parallel network, the feature discrimination ability and generalization ability are enhanced. The model is applied to classify the EEG data in the self-built MEFD dataset, and the results are compared with those of mainstream methods. The experimental results show that the proposed EEG classification method achieves the best average accuracy of 85.36%, precision of 85.23%, recall of 86.35%, and F1 score of 84.52% in the cross-subject emotion recognition task. The excellent performance of GCSFormer in cross-subject emotion recognition task was verifie.},
}

@article {pmid41032544,
year = {2025},
author = {Ju, J and Zhuang, Y and Yi, C},
title = {An EEG-EMG-based Hybrid Brain-Computer Interface for Decoding Tones in Silent and Audible Speech.},
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.2025.3616276},
pmid = {41032544},
issn = {1558-0210},
abstract = {Speech recognition can be widely applied to support people with language disabilities by enabling them to communicate through brain-computer interfaces (BCIs), thus improving their quality of life. Despite the essential role of tonal variations in conveying semantic meaning, there have been limited studies focusing on the neural signatures of tones and their decoding. This paper systematically investigates the neural signatures of the four tones of Mandarin. It explores the feasibility of tone decoding in both silent and audible speech using a multimodal BCI based on electroencephalography (EEG) and electromyography (EMG). The time-frequency analysis of EEG has revealed significant variations in neural activation patterns across various tones and speech modes. For example, in the silent speech condition, temporal-domain analysis shows significant tone-dependent activation in the frontal lobe (ANOVA p = 0.000, Tone1 vs Tone2: p = 0.000, Tone1 vs Tone4: p = 0.000, Tone2 vs Tone3: p = 0.000, Tone3 vs Tone4: p = 0.001) and in channel F8 (ANOVA p=0.008, Tone1 vs Tone2: p=0.014, Tone2 vs Tone3: p=0.034). Spectral analysis shows significant differences between four tones in event-related spectral perturbation (ERSP) in the central region (p = 0.000) and channel C6 (p = 0.000). EMG analysis identifies a significant tone-related difference in activation of the left buccinator muscle (p = 0.023), and ERSP from the mentalis muscle also shows a marked difference across tones in both speech conditions (p = 0.00). Overall, tone-related neural differences were more pronounced in the audible speech condition than in the silent condition. For tone classification, RLDA and SVM classifiers achieved accuracies of 71.22% and 72.43%, respectively, using EEG temporal features in both speech modes. Additionally, the RLDA classifier with temporal features achieves binary tone classification accuracies of 90.92% (audible tones) and 91.00% (silent tones). The combination of EEG and EMG yields the highest speech modes decoding accuracy of 81.33%. These findings provide a potential strategy for speech restoration in tonal languages and further validate the feasibility of a speech brain-computer interface (BCI) as a clinically effective treatment for individuals with tonal language impairment.},
}

@article {pmid41031916,
year = {2025},
author = {Liu, M and Guo, X and Cao, L and Cui, H and Li, Z and Lin, Y and Yin, Z and Quan, W and Feng, C and Ma, T and Zhao, Z and Yang, L and Yao, L and Zhang, X and Wang, G},
title = {Revolutionizing brain-computer interfaces: Compact and high-speed wireless neural signal acquisition.},
journal = {The Review of scientific instruments},
volume = {96},
number = {10},
pages = {},
doi = {10.1063/5.0287033},
pmid = {41031916},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Mice ; Electroencephalography/instrumentation ; Signal-To-Noise Ratio ; *Brain/physiology ; Humans ; },
abstract = {A brain-computer interface (BCI) facilitates the connection between the human brain and external devices by decoding neurophysiological signals, thereby enabling seamless interaction between humans and machines. However, existing neural signal acquisition systems often suffer from limited channel counts, low sampling rates, and challenges in miniaturization and wireless bandwidth, which restrict their ability to support large-scale and real-time neural recordings. Given the rapid advancements in BCI technologies and the increasing demand for high-resolution neural data, there is an imperative need for BCI systems that are high-throughput, high-speed, and miniaturized. This paper presents a wireless neural signal acquisition system based on FPGA technology, supporting 1024 channels at 32 kSPS and employing a stacked architecture for compact, low-power wireless transmission. Following the creation of the functional prototype, laboratory electrical performance tests were conducted. The system exhibited a noise voltage of 8.56 μVrms, which is in close proximity to the 6 μVrms specified by the chip. In addition, the system accurately captured weak sine wave inputs in both time and frequency domains, confirming its ability to record weak bioelectrical signals. Subsequent animal experiments involving mice implanted with EEG electrodes demonstrated that the system could reliably acquire brain neural signals in real time. The maximum and minimum values of signal-to-noise ratios among the channels were measured at 28.66 and 30.56 dB, thereby providing additional validation for the system's signal quality and consistency.},
}

*Brain-Computer Interfaces
*Wireless Technology/instrumentation
Animals
*Signal Processing, Computer-Assisted/instrumentation
Mice
Electroencephalography/instrumentation
Signal-To-Noise Ratio
*Brain/physiology
Humans

@article {pmid41031500,
year = {2025},
author = {Sisubalan, N and Vijay, N and Kesika, P and Newbegin, M and Shalini, R and Sivamaruthi, BS and Chaiysut, C},
title = {The Contribution of Wearable Devices and Artificial Intelligence to Promoting Healthy Aging.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010390500250911104231},
pmid = {41031500},
issn = {1873-4316},
abstract = {INTRODUCTION: Healthy aging involves consistently maximizing opportunities to maintain and enhance physical and mental well-being, fostering independence, and sustaining a high quality of life. This review examines recent technological innovations aimed at promoting the well-being of older adults. The scope encompasses wearable devices and telemedicine, showcasing their potential to enhance the health and overall well-being of older individuals. The review highlights the crucial role of assistive technologies, including mobility aids, hearing aids, and adaptive home devices, in addressing the specific challenges associated with aging.

METHODS: The relevant literature was collected and selected based on the objective of the study and reviewed.

RESULTS: Digital technologies, including brain-computer interfaces (BCIs), are explored as potential solutions to enhance communication between healthcare providers and aging patients, considering engagement levels and active interaction. Sophisticated BCIs, such as electroencephalograms, electrocorticography, and signal modeling for real-time identification, play a crucial role in event detection, with machine learning algorithms enhancing signal processing for accurate decoding. The exploration of smart wearable systems for health monitoring emerges as a dynamic and promising field in the context of aging.

DISCUSSION: Fitbit® showcases accurate step counting, making it suitable for monitoring physical activity in older adults engaged in slow walking. ActiGraph™ is evaluated for accuracy in monitoring physical activity in older adults, with results indicating reliable concurrence with Fitbit® devices. The study identifies several limitations, including sample size constraints, challenges in keeping pace with technological advancements, and the need for further investigation into the suitability of fitness trackers for individuals with significant mobility impairments.

CONCLUSION: The evolving landscape of wearable technologies, exemplified by Fitbit®, Acti- Graph™, and other interventions, holds substantial promise for reshaping healthcare approaches for the aging population. Addressing the limitations will be crucial as research progresses to ensure the effective and ethical integration of wearables into geriatric care, maximizing their potential benefits.},
}

@article {pmid41028971,
year = {2025},
author = {Korkmaz, I and Tepe, C},
title = {EEG-based motor execution classification of upper and lower extremities using machine learning.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2025.2566260},
pmid = {41028971},
issn = {1476-8259},
abstract = {This study classifies upper- and lower-extremity motor execution from electroencephalography (EEG). We compared two feature extractors, statistical features and Common Spatial Patterns (CSP), and four classifiers: K-Nearest Neighbors, Linear Discriminant Analysis (LDA), Multilayer Perceptron, and Support Vector Machine. Metrics were accuracy, F1, precision, and recall. CSP with LDA achieved the best, most consistent performance (72.5% accuracy); statistical features underperformed. We report real-time feasibility benchmarks, post-cue time-window analysis, and significance tests for classifiers. Findings support BCI and neuroprosthesis development, while noting subject variability and dataset specificity. Future work is real-time use, cross-dataset generalization, and hybrid deep learning.},
}

@article {pmid41028569,
year = {2025},
author = {Du, X and Liu, J and Wang, X},
title = {The transformational power of psychedelics: catalysts for creativity, consciousness, and mental health.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41028569},
issn = {1476-5578},
support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; JCYJ20220804182935001//Shenzhen Science and Technology Innovation Commission/ ; },
abstract = {Psychedelics, such as psilocybin, lysergic acid diethylamide (LSD), ketamine, and N,N-dimethyltryptamine (DMT), have captured the attention of scientists, artists, and seekers alike for their profound ability to alter consciousness and inspire creativity. The concept of "creation" encompasses multiple interpretations-ranging from generating novel ideas to fostering personal transformation. This perspective explores how psychedelics interact with the concept of creation, examining their role in enhancing artistic inspiration, facilitating spiritual experiences, and driving therapeutic breakthroughs in mental health treatment. By integrating findings from neurobiological research, clinical applications, and cultural analysis, we offer a holistic view of how psychedelics may catalyze innovative modes of thinking and awaken the mind's creative and transformative potential. As these substances gain prominence as tools for reshaping our understanding of consciousness and psychological healing, their broader integration into society requires careful consideration of legal complexities, ethical responsibilities, and cultural contexts to ensure their use is evidence-based, respectful, and responsibly guided.},
}

@article {pmid41025886,
year = {2025},
author = {Chaudhary, J and Gupta, E and Singh, PK and Yadav, RK and Chaudhary, M and Singh, S},
title = {Designing behavioural change intervention module for tobacco cessation counselling among pregnant tobacco users in India: a methodology paper.},
journal = {Health education research},
volume = {40},
number = {6},
pages = {},
doi = {10.1093/her/cyaf041},
pmid = {41025886},
issn = {1465-3648},
support = {2020-5325//Indian Council of Medical Research, New Delhi/ ; },
mesh = {Humans ; Female ; Pregnancy ; India ; *Counseling/methods ; Prenatal Care/methods ; Adult ; *Tobacco Use Cessation/methods ; *Smoking Cessation/methods ; Tobacco Use ; },
abstract = {Tobacco use has detrimental effects on women's reproductive health and is associated with poor pregnancy outcomes. Antenatal care (ANC) check-ups provide health professionals with a unique opportunity to screen and counsel pregnant tobacco users to quit. Currently, in India, pregnant women are not being screened for tobacco use during antenatal care visits and healthcare providers lack formal training to provide tobacco cessation advice. This article describes the designing and development of a tailored behaviour change intervention (BCI) module for tobacco cessation and its delivery to pregnant women attending antenatal clinics. The BCI module was designed to incorporate the components of the Capability, Opportunity and Motivation Model and the Behaviour Change Wheel guide. The development was done in three steps-understanding the behaviour, developing intervention model, and identifying implementation options along with monitoring and evaluation strategies. The module has three tools-counselling flipbook for healthcare provider, take home pamphlets, and information posters for patient waiting areas. A gender- and culture-specific BCI module was developed and implemented to screen and counsel 105 pregnant tobacco users during antenatal visits, leading to high self-reported tobacco quit rate (69%) which corroborated with urine cotinine levels at baseline and end line.},
}

Humans
Female
Pregnancy
India
*Counseling/methods
Prenatal Care/methods
Adult
*Tobacco Use Cessation/methods
*Smoking Cessation/methods
Tobacco Use

@article {pmid41025122,
year = {2025},
author = {Mohan, A and Anand, RS},
title = {Innovative augmentation techniques and optimized ANN model for imagined speech decoding in EEG-based BCI.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {158},
pmid = {41025122},
issn = {1871-4080},
abstract = {Electroencephalogram (EEG) based Brain computer interface (BCI) emerges as a transformative technology with vast applications in neuroscience and rehabilitation. Imagined speech is the mental process of thinking and formulating words without vocalizing them through articulators. EEG signal is used to study imagined speech which can empower individuals with neurological impairments to communicate their thoughts effortlessly. The main challenge in decoding imagined speech is the nonstationary nature of EEG signals. Identifying robust features and scarcity of imagined speech datasets for properly training machine learning (ML) based algorithms is also a challenging task. The main objective of this study is to propose augmentation methods which mitigate data scarcity in EEG-based BCIs by introducing variations and strengthening model robustness through EEG data augmentation. The second objective is to propose a novel architecture capable of detecting variations in EEG signals for imagined speech datasets and show remarkable results. Seven diverse augmentation techniques are discussed, and the performance of the proposed model is analyzed in terms of accuracy, f1-score and kappa. The classification results are then compared with the case in which no data augmentation is used. The proposed model has shown remarkable accuracy of 91% for long words by using gaussian noise augmentation.},
}

@article {pmid41024222,
year = {2025},
author = {Zhang, Q and Li, W and Zhang, T and Xiong, R and Zhang, J and Jin, Z and Li, L},
title = {Representation of top-down versus bottom-up attention in the right dorsolateral prefrontal cortex and superior parietal lobule.},
journal = {Behavioral and brain functions : BBF},
volume = {21},
number = {1},
pages = {31},
pmid = {41024222},
issn = {1744-9081},
support = {BX202402//Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows/ ; 2025ZNSFSC0453//Sichuan Science and Technology Program/ ; 62176045//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Attention/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Female ; Magnetic Resonance Imaging/methods ; Adult ; Young Adult ; *Dorsolateral Prefrontal Cortex/physiology/diagnostic imaging ; Brain Mapping/methods ; Visual Perception/physiology ; Photic Stimulation/methods ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; },
abstract = {BACKGROUND: Visual selective attention can be categorized into top-down (goal-driven) and bottom-up (stimulus-driven) attention, with the fronto-parietal network serving as the primary neural substrate. However, fewer studies have focused on the specific roles of the right dorsolateral prefrontal cortex (DLPFC) and superior parietal lobule (SPL) in top-down and bottom-up attention. This study aimed to investigate the activity and connectivity of the right DLPFC and SPL in top-down and bottom-up attention.

METHODS: Visual pop-out task mainly induces bottom-up attention, while the visual search task mainly induces top-down attention. Fifty-four participants completed the pop-out and search tasks during functional magnetic resonance imaging (fMRI) scanning. We used univariate analyses, multivariate pattern analyses (MVPA), and generalized psychophysiological interaction (gPPI) to assess activity and functional connectivity.

RESULTS: Univariate analyses revealed stronger activation in the right DLPFC and SPL during the search > pop-out condition. The activation of the DLPFC was driven by its deactivation in the pop-out task, whereas the SPL showed significant activation in both tasks. MVPA demonstrated that activation patterns in the right DLPFC and SPL could distinguish between the pop-out and search tasks above chance level (0.5), with the right SPL exhibiting higher classification accuracy. The gPPI analyses showed that higher functional connectivity between the two seeds (right DLPFC and SPL) and bilateral precentral gyrus, left SPL, and right insula.

CONCLUSIONS: These results indicate that the right DLPFC and SPL showed stronger activity and connectivity under top-down versus bottom-up attention, allowing for neural representation of visual selective attention. This study provides evidence for understanding the role of the fronto-parietal network in visual selective attention.},
}

Humans
*Attention/physiology
*Parietal Lobe/physiology/diagnostic imaging
Male
Female
Magnetic Resonance Imaging/methods
Adult
Young Adult
*Dorsolateral Prefrontal Cortex/physiology/diagnostic imaging
Brain Mapping/methods
Visual Perception/physiology
Photic Stimulation/methods
*Prefrontal Cortex/physiology
Neural Pathways/physiology

@article {pmid41022774,
year = {2025},
author = {Li, L and Hartzler, A and Menendez-Lustri, DM and Zhang, J and Chen, A and Lam, DV and Traylor, B and Quill, E and Nethery, DE and Hoeferlin, GF and Pawlowski, CL and Bruckman, MA and Sen Gupta, A and Capadona, JR and Shoffstall, AJ},
title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8579},
pmid = {41022774},
issn = {2041-1723},
support = {GRANT12635707//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; HL121212//U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)/ ; T32EB004314//U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; },
mesh = {Animals ; *Dexamethasone/administration & dosage/analogs & derivatives/pharmacology ; Microelectrodes/adverse effects ; Blood-Brain Barrier/drug effects/metabolism ; Rats ; *Nanoparticles/chemistry ; Male ; *Blood Platelets/chemistry ; Neurons/drug effects ; Rats, Sprague-Dawley ; Electrodes, Implanted ; Brain-Computer Interfaces ; Drug Delivery Systems ; Anti-Inflammatory Agents/administration & dosage ; },
abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by IME insertions contribute to increased neuroinflammation and reduced neural recording performance. Here, we evaluated dexamethasone sodium phosphate-loaded platelet-inspired nanoparticles (DEXSPPIN) to simultaneously augment local hemostasis and serve as an implant-site targeted drug-delivery vehicle. Weekly systemic treatment or control therapy was provided to rats for 8 weeks following IME implantation, while evaluating extracellular single-unit recording performance. End-point immunohistochemistry was performed to further assess the local tissue response to the IMEs. Treatment with DEXSPPIN significantly increased the recording capabilities of IMEs compared to controls over the 8-week observation period. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggested that the improved neural recording performance may be attributed to reduced neuron degeneration and neuroinflammation. Overall, we found that DEXSPPIN treatment promoted an anti-inflammatory environment that improved neuronal density and enhanced IME recording performance.},
}

Animals
*Dexamethasone/administration & dosage/analogs & derivatives/pharmacology
Microelectrodes/adverse effects
Blood-Brain Barrier/drug effects/metabolism
Rats
*Nanoparticles/chemistry
Male
*Blood Platelets/chemistry
Neurons/drug effects
Rats, Sprague-Dawley
Electrodes, Implanted
Brain-Computer Interfaces
Drug Delivery Systems
Anti-Inflammatory Agents/administration & dosage

@article {pmid41022701,
year = {2025},
author = {Li, J and Li, L and Gao, Z and Tian, Y},
title = {Molecular Dynamics and Neural Network Analysis Reveal Sequential Gating and Allosteric Communication in FMRFamide-Activated Sodium Channels.},
journal = {Journal of chemical information and modeling},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jcim.5c01255},
pmid = {41022701},
issn = {1549-960X},
abstract = {FMRFamide-activated sodium channels (FaNaCs) represent a unique class of neuropeptide-gated ion channels within the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. While cryo-electron microscopy has revealed static binding architectures, the dynamic mechanisms underlying ligand recognition, allosteric signal transmission, and channel gating remain poorly understood. Here, we employed microsecond-scale molecular dynamics simulations coupled with neural relational inference analysis to elucidate the complete activation mechanism of FaNaC at atomic resolution. Our analysis revealed a sophisticated multistage activation process initiated by coordinated dynamics of FaNaC-specific insertions SI1 and SI2. Spontaneous FMRFamide-binding events suggested that SI1 functions as a dynamic gate that facilitates optimal ligand burial and stabilization, while SI2 appeared to serve as a conformational lid stabilizing the bound ligand through thermodynamically favorable induced-fit mechanisms. This ligand-induced conformational change, which involves the cooperative reorganization of the three peripheral loops (L1, L2, and L3) in the extracellular domain, propagates through the extracellular domain, particularly via a coordinated rigid-body motion of the β-ball/palm domain, leading to the reorganization of the central β-sheet in the extracellular vestibule and a subsequent conformational wave that compacts the intracellular vestibule. We further leveraged neural relational inference (NRI) to analyze residue-level allosteric networks, demonstrating that ligand binding enhances the network's connectivity and reorganizes allosteric communication pathways. These findings provide a high-resolution, dynamic view of FaNaC function, revealing a novel gating mechanism for the DEG/ENaC superfamily and laying the foundation for future studies into neuropeptide modulation.},
}

@article {pmid41022567,
year = {2025},
author = {Chen, X and Cao, L and Wieske, RE and Prada, J and Gramann, K and Haendel, BF},
title = {Walking modulates active auditory sensing.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0489-25.2025},
pmid = {41022567},
issn = {1529-2401},
abstract = {Walking provides the motor foundation for navigation, while navigation ensures that walking is purposeful and adaptive to environmental contexts. Sensory processing of environmental information acts as the informational bridge that connects walking and adaptive navigation. In the current study, we assessed if walking and the walking direction influences neuronal dynamics underlying environmental information processing. To this end, we conducted two experiments with 12 male and 18 female participants while they walked along an 8-shaped path. Auditory entrainment stimuli were continuously presented, and mobile EEG (electroencephalogram) was recorded. We found increased auditory entrainment (auditory steady-state response) and early auditory evoked responses during walking compared to standing or stepping-in-place. We also replicated the well-established reduction of occipital alpha power during walking. The increase of auditory entrainment and the decrease of alpha power were correlated across participants. In the second experiment, randomly presented transient burst tones led to a perturbation of the auditory entrainment response. The perturbation response was stronger during walking compared to standing, however, only when the burst tones were presented to one ear but not to both ears. Most importantly, we found that the auditory entrainment was systematically modulated dependent on the walking path. The entrainment responses changed as a function of the turning direction. In general, the current work shows that walking changes auditory processing in a walking path-dependent way which might serve to optimize navigation. The walking path related modulation might further reflect a shift of attention, marking a form of higher-order active sensing.Significance Statement In this mobile EEG walking study, we uncovered a dynamic shift in auditory attention that aligns with changes in walking trajectory. Specifically, during turns, the brain prioritizes auditory input from the side of turn direction before the turn apex, then shifts preference to the opposite side. These findings reveal an active sensing mechanism that goes beyond simple motor adjustments to adjust sensory input but suggests that the brain dynamically optimizes the processing of sensory input e.g. to facilitate navigation. This study offers potential applications for understanding spatial awareness in real-world environments and improving navigational aids.},
}

@article {pmid41022118,
year = {2025},
author = {Han, Y and Wang, S},
title = {E-Sort: empowering end-to-end neural network for multi-channel spike sorting with transfer learning and fast post-processing.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0d33},
pmid = {41022118},
issn = {1741-2552},
abstract = {OBJECTIVE: Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording technologies are challenging the conventional algorithms because of the intensive computational workloads required and the accuracy degradation suffered from time-variant spike patterns and significant levels of noise. Neural networks have demonstrated promising performance in processing these large-scale neural recordings. However, their applications are constrained by the labor-intensive data labeling and the lack of fully vectorised frameworks with end-to-end neural networks.

APPROACH: We propose E-Sort, an end-to-end neural network-based spike sorter with transfer learning and parallelizable post-processing to address both obstacles.

MAIN RESULTS: We examined our framework in both synthetic and real datasets. The results of the processing of the synthetic datasets show that our approach can reduce the number of annotated spikes required for training by 44% compared to training from scratch, achieving up to 25.7% higher accuracy. We evaluated E-Sort on various probe geometries, noise levels, and drift patterns, which demonstrates that our design can achieve an accuracy that is comparable with Kilosort4 while sorting 50 seconds of data in only 1.32 seconds. To test with real datasets, we first sorted the spikes using Kilosort4 and used the sorted spikes at the initial period to pre-train the neural network; then we compared and measured the agreement between the results from the trained model and those from Kilosort4. On average the pre-training process improved the result agreement by 30% approximately.

SIGNIFICANCE: E-Sort offers a scalable, efficient, and accurate neural network-based framework for large-scale spike sorting, significantly reducing manual labelling effort and processing time.},
}

@article {pmid41021940,
year = {2025},
author = {Bulfer, S and Gamez, J and Yan-Huang, A and Haghi, B and Pedroni, V and Andersen, RA and Emami, A},
title = {A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3615121},
pmid = {41021940},
issn = {1940-9990},
abstract = {We present a 192-channel 1D convolutional neural network (1D CNN) based neural feature extractor for Brain-Machine Interfaces (BMI) that achieves state-of-the-art decoding stability at 1.8 $μ$W and 12801 $μ$m[2] per channel in 65nm CMOS technology. Our device is a fully configurable, scalable, area and power efficient solution that supports models with 2-8 feature layers and a total kernel length of up to 256. This architecture reduces caching requirements by 5× over conventional computation schemes. Channels and layers are individually power-switchable to further optimize power efficiency for a given neural application. We introduce an on-chip model, FENet-66, that achieves the highest cross-validated decoding performance compared to all previously reported feature sets. We show that this model maintains superior stability over time using recorded data from tetraplegic human participants with spinal cord injury. Our features have 18% higher overall average cross-validated R2 decoding performance compared to Spiking Band Power (SBP), with 28% better performance during the 4th year. Our proposed architecture can also extract mean wavelet power features at low power and latency. We show that custom 1D-CNN kernels achieve 10% better performance compared to wavelet features while compressing the neural data stream by 38×. The models and hardware were validated in real time with a human subject in online closed-loop center-out cursor control experiments with micro-electrode arrays that were implanted for 6 years. Decoders using features generated with this work substantially improve the viability of longterm neural implants compared to other feature extraction methods currently present in low power BMI hardware.},
}

@article {pmid41021638,
year = {2025},
author = {Ferrea, E and Morel, P and Gail, A},
title = {Frontal and parietal planning signals encode adapted motor commands when learning to control a brain-computer interface.},
journal = {PLoS biology},
volume = {23},
number = {9},
pages = {e3003408},
doi = {10.1371/journal.pbio.3003408},
pmid = {41021638},
issn = {1545-7885},
abstract = {Perturbing visual feedback is a powerful tool for studying visuomotor adaptation. However, unperturbed proprioceptive signals in common paradigms inherently co-varies with physical movements and causes incongruency with the visual input. This can create challenges when interpreting underlying neurophysiological mechanisms. We employed a brain-computer interface (BCI) in rhesus monkeys to investigate spatial encoding in frontal and parietal areas during a 3D visuomotor rotation task where only visual feedback was movement-contingent. We found that both brain regions better reflected the adapted motor commands than the perturbed visual feedback during movement preparation and execution. This adaptive response was observed in both local and remote neurons, even when they did not directly contribute to the BCI input signals. The transfer of adaptive changes in planning activity to corresponding movement corrections was stronger in the frontal than in the parietal cortex. Our results suggest an integrated large-scale visuomotor adaptation mechanism in a motor-reference frame spanning across frontoparietal cortices.},
}

@article {pmid41021378,
year = {2025},
author = {de Camargo, PS and Santos E Souza, GO and Arévalo, A and Lepski, G},
title = {Intraoperative Techniques for Language Mapping in Brain Surgery: A Comparison Between Direct Electrical Stimulation (DES) and Electrocorticography (ECoG).},
journal = {Brain and behavior},
volume = {15},
number = {10},
pages = {e70900},
pmid = {41021378},
issn = {2162-3279},
support = {2018/18900-1//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; 2023/17520-9//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; },
mesh = {Humans ; *Electrocorticography/methods ; *Language ; *Brain Mapping/methods ; *Intraoperative Neurophysiological Monitoring/methods ; *Electric Stimulation/methods ; *Brain/surgery ; Neurosurgical Procedures/methods ; },
abstract = {PURPOSE: The purpose of this overview is to compare Direct Electrical Stimulation (DES) and Electrocorticography (ECoG) techniques, assessing their respective strengths, limitations, and roles in ensuring successful language mapping during awake brain surgeries.

METHOD: This overview aims to compare two techniques used in intraoperative language mapping during awake brain surgery: Direct Electrical Stimulation (DES) and Electrocorticography (ECoG). By summarizing recent advances in both methods, we highlight their respective mechanisms, applications, and roles in improving surgical outcomes. DES is widely considered the gold standard for cortical brain mapping and is applicable in both awake and anesthetized surgeries for treating epilepsy and brain tumors. In contrast, ECoG involves monitoring the brain's electrical activity with or without direct stimulation, as it provides valuable insight into high gamma activity (70-150 Hz), which is strongly associated with speech production.

FINDING: ECoG offers a high-resolution approach to language mapping by detecting high-gamma activity, reducing the risk of intraoperative seizures, and serving as a complementary or alternative tool to DES in specific clinical scenarios. While DES continues to be the most reliable technique for identifying functional brain areas, it does carry a higher risk of inducing seizures. Furthermore, recent advancements in ECoG-based speech decoding and brain-computer interfaces (BCIs) underscore the growing potential of ECoG in restoring communication in patients with severe language impairments, extending its applications beyond surgical mapping.

CONCLUSION: In conclusion, while DES remains the gold standard for intraoperative language mapping, ECoG is emerging as a promising complementary or alternative technique in some clinical cases. This overview highlights the evolving role of ECoG, particularly in the context of speech decoding and BCIs, offering new possibilities for improving surgical outcomes and postoperative quality of life in patients.},
}

Humans
*Electrocorticography/methods
*Language
*Brain Mapping/methods
*Intraoperative Neurophysiological Monitoring/methods
*Electric Stimulation/methods
*Brain/surgery
Neurosurgical Procedures/methods

@article {pmid40962534,
year = {2025},
author = {Wang, Y},
title = {[Promote the application and innovation of artificial intelligence in pediatric neurological diseases].},
journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics},
volume = {63},
number = {10},
pages = {1045-1047},
doi = {10.3760/cma.j.cn112140-20250722-00671},
pmid = {40962534},
issn = {0578-1310},
mesh = {Humans ; *Artificial Intelligence ; Child ; *Nervous System Diseases/diagnosis/therapy ; Machine Learning ; *Pediatrics/methods ; Brain-Computer Interfaces ; Decision Support Systems, Clinical ; },
}

Humans
*Artificial Intelligence
Child
*Nervous System Diseases/diagnosis/therapy
Machine Learning
*Pediatrics/methods
Brain-Computer Interfaces
Decision Support Systems, Clinical

@article {pmid41017975,
year = {2025},
author = {Adama, S and Bogdan, M},
title = {Assessing consciousness in patients with locked-in syndrome using their EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1604173},
pmid = {41017975},
issn = {1662-4548},
abstract = {Research indicates that locked-in syndrome (LIS) patients retain both consciousness and cognitive functions, despite their inability to perform voluntary muscle movements or communicate. Brain-Computer Interfaces (BCIs) provide a means for these patients to communicate, which is crucial, as the ability to interact with their environment has been shown to significantly enhance their wellbeing and quality of life. This paper presents an innovative approach to analyzing electroencephalogram (EEG) data from four LIS patients to assess their consciousness levels, referred to as normalized consciousness levels (NCL) in this study. It consists of extracting different features based on frequency, complexity, and connectivity measures to maximize the probability of correctly determining the patients' actual states given the inexistence of ground truth. The consciousness levels derived from this approach aim to improve our understanding of the patients' condition, which is vital in order to build effective communication systems. Despite considerable inter-patient variability, the findings indicate that the approach is effective in detecting neural markers of consciousness and in differentiating between states across the majority of patients. By accurately assessing consciousness, this research aims to improve diagnosis in addition to determining the optimal time to initiate communication with these non-communicative patients. It is important to note that consciousness is a complex and difficult concept to define. In this study, the term "consciousness level" does not refer to a medical definition. Instead, it represents a scale of NCL values ranging from 0 to 1 representing the likelihood of the patient being fully conscious (1) or not (0).},
}

@article {pmid41017235,
year = {2025},
author = {Chen, D and Lu, Y and Zhang, S and Zhang, W and Yu, Z and Wang, S and Qu, Z and Cheng, M and Yao, Y and Wang, D and Yang, Z and Dong, L},
title = {An Ultra-Flexible Neural Electrode with Bioelectromechanical Compatibility and Brain Micromotion Detection.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e03101},
doi = {10.1002/adhm.202503101},
pmid = {41017235},
issn = {2192-2659},
support = {62127810//National Natural Science Foundation of China/ ; CityU11213720//Research Grants Council of the Hong Kong Special Administrative Region/ ; CityU11217221//Research Grants Council of the Hong Kong Special Administrative Region/ ; 9680347//City University of Hong Kong/ ; 9610608//City University of Hong Kong/ ; 9680103//City University of Hong Kong/ ; },
abstract = {Neural electrodes, as core components of brain-computer interfaces(BCIs), face critical challenges in achieving stable mechanical coupling with brain tissue to ensure high-quality signal acquisition. Current flexible electrodes, including semi-invasive meningeal-attached types and implantable cantilever designs, exhibit significant mechanical mismatches (elastic modulus 5-6 orders higher than brain tissue) due to material/structural limitations, leading to interfacial slippage. While thread-like implants (e.g., Neuralink's electrodes) improve compliance via elongated structures, quantitative characterization of mechano-bioelectric interactions remains unexplored. This study proposes a bioelectromechanical coupling strategy, emphasizing synchronized motion between the electrode and the brain tissue through exposed-end deformation. A 4-channel ultra-flexible electrode (40 mm in length, 164 µm in width, and 3 µm in thickness) is optimized using finite-element simulations and zero relative-motion criteria, achieving an equivalent stiffness of 0.023 N m[-1]-matching brain tissue micromotion stiffness. A nanorobotic manipulator installed inside a scanning electron microscope(SEM) with an atomic force microscope(AFM) cantilever enabled precision characterization under the simulated displacement of 25 µm, revealing interfacial forces of 575 nN and piezoresistive sensitivities of 6.4 pA mm[-1] (length) and 10.2 pA µm[-1] (displacement). The dual-functionality (signal acquisition and micromotion sensing) electrodes demonstrate breakthrough potential, establishing quantitative design standards for next-generation bioelectronic implants.},
}

@article {pmid41016568,
year = {2025},
author = {Li, J and Yang, W and Liu, X and Yang, K and Zhou, J and Yang, X},
title = {Research progress of lung organoids in infectious respiratory diseases.},
journal = {European journal of pharmacology},
volume = {},
number = {},
pages = {178201},
doi = {10.1016/j.ejphar.2025.178201},
pmid = {41016568},
issn = {1879-0712},
abstract = {Infectious respiratory diseases are common epidemics that often exhibit phased outbreaks, increasing the healthcare burden. Past research models for these diseases were relatively simplistic, but the emergence of organoids has transformed this landscape. Organoids, three-dimensional in vitro tissue analogs that recapitulate specific spatial organ structures derived from stem cell culture, have advanced significantly over the decade since their inception. Compared to conventional animal models, organoids circumvent interspecies variations, enabling a more precise representation of human physiological and pathological traits. Relative to two-dimensional cell cultures, organoids exhibit enhanced complexity, incorporating diverse cell types and maintaining stable genomes, which facilitates a more faithful simulation of cellular interactions within the extracellular microenvironment. Consequently, as a three-dimensional in vitro model, lung organoids are pivotal for investigating lung organ development, infectious disease pathogenesis, and drug screening. Although SARS-CoV-2 is receding from the spotlight, advancing lung organoid development for addressing infectious respiratory diseases like influenza remains a priority. This review demonstrated the differentiation culture process of lung organoids and outlined advancements in utilizing organoids to elucidate pathogenic infection mechanisms, reveal virus-host interactions and screen therapeutic drugs over the past seven years. Additionally, we have summarized the advances in lung organoid model technologies and outlined their developmental directions.},
}

@article {pmid41016446,
year = {2025},
author = {Wang, L and An, X and Jiang, Z and Wang, J and Ming, D},
title = {The Individual Differences Analysis of Audiovisual Bounce-inducing Effects.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115851},
doi = {10.1016/j.bbr.2025.115851},
pmid = {41016446},
issn = {1872-7549},
abstract = {The audiovisual bounce-inducing effect (ABE) is a phenomenon that the brain integrates spatial and temporal information from different sensory modalities of vision and hearing. At present, some researchers have conducted research on the individual differences of the ABE, but have not considered the factor of audiovisual stimulus intervals. This study investigated the neural mechanisms underlying the intra- and inter-individual differences in subjects' ABE at different audiovisual stimulus onset asynchronies (SOAs). This study adopted the experimental paradigm of Stream/Bounce illusion, in which visual and auditory stimuli were presented in 7 different SOAs. We recorded behavioral and EEG data during the experiment, compared and analyzed the amplitude differences of event-related potentials (ERPs), calculated statistical indicators, and studied the intra- and inter-individual differences of the ABE under different SOAs. The results show that in terms of the inter-individual differences in the ABE, the amplitude of N1 is more significant in the High ABE Group than the Low ABE Group at SOAs of "V100A" and "0". Individual ABE tendencies are also significantly correlated with N1 amplitude at the two SOAs. These results reveal the effect of stimuli interval on the processing of audiovisual stimuli, there is a complex interplay between the individual's sensory processing mechanisms and the specific temporal dynamics of audiovisual integration.},
}

@article {pmid41015681,
year = {2025},
author = {Parodi, F and Kording, KP and Platt, ML},
title = {Primate neuroethology: a new synthesis.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.09.002},
pmid = {41015681},
issn = {1879-307X},
abstract = {Neuroscience has probed only a sliver of the rich cognitive, emotional, and social behaviors that enable primates to thrive in the real world. Technological breakthroughs allow us to quantify these behaviors alongside wireless neural recordings. New studies reveal that neural activity is intricately bound to movement and is profoundly modulated by behavioral context, emotional states, and social dynamics. We frame our review of primate neuroethology around Niko Tinbergen's four foundational questions - function, mechanism, development, and evolution - to unify classic ethological insights with modern neuroscience tools. We demonstrate that investigating natural behavior promises deep insights into primate cognition, which are relevant for advanced brain-machine interfaces, improved therapies for neurological disorders, and deeper understanding of natural and artificial intelligence.},
}

@article {pmid39718390,
year = {2025},
author = {Gordon, CR and Perez, CF},
title = {Review of Functional Cranioplasty and Implantable Neurotechnology.},
journal = {The Journal of craniofacial surgery},
volume = {36},
number = {2},
pages = {387-393},
doi = {10.1097/SCS.0000000000011028},
pmid = {39718390},
issn = {1536-3732},
mesh = {Humans ; *Skull/surgery ; *Prostheses and Implants ; *Plastic Surgery Procedures/methods/instrumentation ; Prosthesis Design ; *Craniotomy/methods ; },
abstract = {Cranioplasty for secondary reconstruction of cranial defects has historically focused on simply replacing the missing cranial bone to restore cerebral protection and fluid dynamics, but recent innovations have led to the development of customized cranial implants that address both bone and soft tissue deficits while avoiding postoperative complications such as temporal hollowing. In addition, customized cranial implants have incorporated implantable neurotechnology like ventriculostomy shunts, intracranial pressure monitoring devices, and medicine delivery systems within low-profile designs to convert previously "basic" implants into "smart" implants for added functionality. These "smart" implants aim to reduce complications and improve patient outcomes by leveraging the cranial space to house advanced technologies, providing benefits such as real-time biosensing, and treatment of chronic neurological conditions. This review outlines the progression of cranioplasty from basic bone replacement to functional implants with embedded neurotechnologies, highlighting the multidisciplinary approaches that enhance surgical outcomes and patient quality of life.},
}

Humans
*Skull/surgery
*Prostheses and Implants
*Plastic Surgery Procedures/methods/instrumentation
Prosthesis Design
*Craniotomy/methods

@article {pmid41011900,
year = {2025},
author = {Tan, X and Tong, B and Zhang, K and Ni, C and Yang, D and Gao, Z and Huang, Y and Yao, N and Huang, L},
title = {Mechanical Behavior Analysis of Neural Electrode Arrays Implantation in Brain Tissue.},
journal = {Micromachines},
volume = {16},
number = {9},
pages = {},
pmid = {41011900},
issn = {2072-666X},
support = {2023BAA005//Major Program (JD) of Hubei Province(2023BAA005)./ ; },
abstract = {Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design.},
}

@article {pmid41009567,
year = {2025},
author = {Haghighi, P and Smith, TJ and Tahmasebi, G and Vargas, S and Jiang, MS and Massaquoi, AC and Huff, J and Capadona, JR and Pancrazio, JJ},
title = {Piezo1 and Piezo2 Ion Channels in Neuronal and Astrocytic Responses to MEA Implants in the Rat Somatosensory Cortex.},
journal = {International journal of molecular sciences},
volume = {26},
number = {18},
pages = {},
pmid = {41009567},
issn = {1422-0067},
support = {1R01NS110823-06/NH/NIH HHS/United States ; },
mesh = {Animals ; *Ion Channels/metabolism/genetics ; *Somatosensory Cortex/metabolism/cytology ; *Astrocytes/metabolism ; Rats ; Microelectrodes/adverse effects ; *Neurons/metabolism ; Male ; Electrodes, Implanted/adverse effects ; Rats, Sprague-Dawley ; },
abstract = {Intracortical microelectrode arrays (MEAs) are tools for recording and stimulating neural activity, with potential applications in prosthetic control and treatment of neurological disorders. However, when chronically implanted, the long-term functionality of MEAs is hindered by the foreign body response (FBR), characterized by gliosis, neuronal loss, and the formation of a glial scar encapsulating layer. This response begins immediately after implantation and is exacerbated by factors such as brain micromotion and the mechanical mismatch between stiff electrodes and soft brain tissue, leading to signal degradation. Despite progress in mitigating these issues, the underlying mechanisms of the brain's response to MEA implantation remain unclear, particularly regarding how cells sense and respond to the associated mechanical forces. Mechanosensitive ion channels, such as the Piezo family, are key mediators of cellular responses to mechanical stimuli. In this study, silicon-based NeuroNexus MEAs consisting of four shanks were implanted in the rat somatosensory cortex for sixteen weeks. Weekly neural recordings were conducted to assess signal quality over time, revealing a decline in active electrode yield and signal amplitude. Immunohistochemical analysis showed an increase in GFAP intensity and decreased neuronal density near the implant site. Furthermore, Piezo1-but not Piezo2-was strongly expressed in GFAP-positive astrocytes within 25 µm of the implant. Piezo2 expression appeared relatively uniform within each brain slice, both in and around the MEA implantation site across cortical layers. Our study builds on previous work by demonstrating a potential role of Piezo1 in the chronic FBR induced by MEA implantation over a 16-week period. Our findings highlight Piezo1 as the primary mechanosensitive channel driving chronic FBR, suggesting it may be a target for improving MEA design and long-term functionality.},
}

Animals
*Ion Channels/metabolism/genetics
*Somatosensory Cortex/metabolism/cytology
*Astrocytes/metabolism
Rats
Microelectrodes/adverse effects
*Neurons/metabolism
Male
Electrodes, Implanted/adverse effects
Rats, Sprague-Dawley

@article {pmid41008372,
year = {2025},
author = {Finnis, R and Mehmood, A and Holle, H and Iqbal, J},
title = {Exploring Imagined Movement for Brain-Computer Interface Control: An fNIRS and EEG Review.},
journal = {Brain sciences},
volume = {15},
number = {9},
pages = {},
doi = {10.3390/brainsci15091013},
pmid = {41008372},
issn = {2076-3425},
abstract = {Brain-Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning-particularly deep learning-have improved the feasibility of online MI decoding. Hybrid EEG-fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies.},
}

@article {pmid41008292,
year = {2025},
author = {Hasegawa, RP and Watanabe, S},
title = {Neurodetector: EEG-Based Cognitive Assessment Using Event-Related Potentials as a Virtual Switch.},
journal = {Brain sciences},
volume = {15},
number = {9},
pages = {},
doi = {10.3390/brainsci15090931},
pmid = {41008292},
issn = {2076-3425},
support = {A19-46//AMED/ ; JP24K 12215//JSPS KAKENHI/ ; },
abstract = {Background/Objectives: Motor decline in older adults can hinder cognitive assessments. To address this, we developed a brain-computer interface (BCI) using electroencephalography (EEG) and event-related potentials (ERPs) as a motor-independent EEG Switch. ERPs reflect attention-related neural activity and may serve as biomarkers for cognitive function. This study evaluated the feasibility of using ERP-based task success rates as indicators of cognitive abilities. The main goal of this article is the development and baseline evaluation of the Neurodetector system (incorporating the EEG Switch) as a motor-independent tool for cognitive assessment in healthy adults. Methods: We created a system called Neurodetector, which measures cognitive function through the ability to perform tasks using a virtual one-button EEG Switch. EEG data were collected from 40 healthy adults, mainly under 60 years of age, during three cognitive tasks of increasing difficulty. Results: The participants controlled the EEG Switch above chance level across all tasks. Success rates correlated with task difficulty and showed individual differences, suggesting that cognitive ability influences performance. In addition, we compared the pattern-matching method for ERP decoding with the conventional peak-based approaches. The pattern-matching method yielded a consistently higher accuracy and was more sensitive to task complexity and individual variability. Conclusions: These results support the potential of the EEG Switch as a reliable, non-motor-dependent cognitive assessment tool. The system is especially useful for populations with limited motor control, such as the elderly or individuals with physical disabilities. While Mild Cognitive Impairment (MCI) is an important future target for application, the present study involved only healthy adult participants. Future research should examine the sources of individual differences and validate EEG switches in clinical contexts, including clinical trials involving MCI and dementia patients. Our findings lay the groundwork for a novel and accessible approach for cognitive evaluation using neurophysiological data.},
}

@article {pmid41006944,
year = {2025},
author = {Huang, W and Li, H and Qin, F and Wu, D and Cheng, K and Chen, H},
title = {A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550068},
doi = {10.1142/S0129065725500686},
pmid = {41006944},
issn = {1793-6462},
abstract = {Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.},
}

@article {pmid41006379,
year = {2025},
author = {Altaheri, H and Karray, F and Karimi, AH},
title = {Temporal convolutional transformer for EEG based motor imagery decoding.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32959},
pmid = {41006379},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) offer a transformative pathway for rehabilitation, communication, and control by translating imagined movements into actionable commands. However, accurately decoding motor imagery from electroencephalography (EEG) signals remains a significant challenge in BCI research. In this paper, we propose TCFormer, a temporal convolutional Transformer designed to improve the performance of EEG-based motor imagery decoding. TCFormer integrates a multi-kernel convolutional neural network (MK-CNN) for spatial-temporal feature extraction with a Transformer encoder enhanced by grouped query attention to capture global contextual dependencies. A temporal convolutional network (TCN) head follows, utilizing dilated causal convolutions to enable the model to learn long-range temporal patterns and generate final class predictions. The architecture is evaluated on three benchmark motor imagery and motor execution EEG datasets: BCIC IV-2a, BCIC IV-2b, and HGD, achieving average accuracies of 84.79, 87.71, and 96.27%, respectively, outperforming current methods. These results demonstrate the effectiveness of the integrated design in addressing the inherent complexity of EEG signals. The code is publicly available at https://github.com/altaheri/TCFormer .},
}

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

@article {pmid41005779,
year = {2025},
author = {Kawakami, DMO and Karloh, M and Araujo, GHG and Colucci, MG and Pires Di Lorenzo, VA and Mendes, RG},
title = {Effects of an early behavioural change strategy following COPD exacerbation in hospital and outpatient settings in Brazil: protocol for a randomised clinical trial on cardiovascular risk, physical activity and functionality.},
journal = {BMJ open},
volume = {15},
number = {9},
pages = {e097954},
doi = {10.1136/bmjopen-2024-097954},
pmid = {41005779},
issn = {2044-6055},
mesh = {Humans ; *Pulmonary Disease, Chronic Obstructive/rehabilitation/psychology/physiopathology/complications ; Brazil ; Quality of Life ; *Exercise ; Randomized Controlled Trials as Topic ; Cost-Benefit Analysis ; *Cardiovascular Diseases/prevention & control ; Disease Progression ; *Behavior Therapy/methods ; Outpatients ; },
abstract = {INTRODUCTION: Patients living with chronic obstructive pulmonary disease (COPD) experience periods of disease stability and exacerbations (ECOPD). COPD imposes a negative and impactful extrapulmonary impairment and commonly overlaps with multimorbidity, particularly cardiovascular disease. Pulmonary rehabilitation (PR) aims to improve physical activity (PA) and quality of life, while behavioural change interventions (BCIs) aim to promote lifestyle changes and autonomy. However, after ECOPD, a variety of barriers often delay patient referral to PR. This study aims to assess the effects of a BCI for patients after ECOPD, focusing on cardiovascular health, PA and functionality. Additionally, the study will assess 6-month sustainability of PA and conduct a cost-utility analysis comparing a non-intervention group in the Unified Health System.

METHODS AND ANALYSIS: This randomised clinical trial will assess patients with ECOPD over 12 weeks using a BCI based on self-determination theory to increase daily steps. First, the cardiovascular and functional profile will be evaluated. Afterwards, the patients will receive an accelerometer to monitor the PA level. After 7 days, questionnaires will be applied on quality of life, symptoms and motivational levels for PA. Patients will be randomised into control group or intervention groups, both will receive educational booklets and IG will also receive an educational interview. PA will be tracked using activPAL accelerometer at weeks 1, 4 and 12, and follow-up at 6 months. Data analysis will include unpaired Student's t-test or Mann-Whitney test for group comparison, and a linear mixed model to assess intervention effects over time. Economic evaluation, using STATA (V.14), will involve correlation analysis, and p<0.05 significance will be considered.

ETHICS AND DISSEMINATION: This study has been approved by the Federal University of São Carlos' Ethics Committee, Irmandade Santa Casa de Misericórdia de São Carlos and Base Hospital of São José do Rio Preto. All procedures will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines and applicable regulatory requirements. All results will be presented in peer-reviewed medical journals and international conferences.

TRIAL REGISTRATION NUMBER: Brazilian Registry of Clinical Trials under the registration number RBR-6m9pwb7.},
}

Humans
*Pulmonary Disease, Chronic Obstructive/rehabilitation/psychology/physiopathology/complications
Brazil
Quality of Life
*Exercise
Randomized Controlled Trials as Topic
Cost-Benefit Analysis
*Cardiovascular Diseases/prevention & control
Disease Progression
*Behavior Therapy/methods
Outpatients

@article {pmid41005749,
year = {2025},
author = {Zhang, H and Xie, J and Yu, H and Du, F and Jin, Z and Chen, Y},
title = {Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110589},
doi = {10.1016/j.jneumeth.2025.110589},
pmid = {41005749},
issn = {1872-678X},
abstract = {BACKGROUND: Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.

NEW METHOD: This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.

RESULTS: The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.

Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.

CONCLUSIONS: Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.},
}

@article {pmid41005327,
year = {2025},
author = {Wang, N and Deng, X and Zhu, N and Wang, X and Wang, Y and Sun, B and Zheng, C},
title = {Bayesian decoding and its application in reading out spatial memory from neural ensembles.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0c3c},
pmid = {41005327},
issn = {1741-2552},
abstract = {Spatial memory serves as a foundation to establish cognitive map, supporting navigation and decision-making processes across species. Essential brain regions such as the hippocampus and entorhinal cortex enable these functions through spatially tuned neurons, particularly place cells, which encode an animal's precise location. The continuous spatial trajectories are then able to be represented by temporally sequential firing of these cells at neural ensemble level. Bayesian frameworks are powerful tools for reconstructing such "mind travel". In this article, we focus on the principles and advances of Bayesian decoding methods for extracting spatial memory information from neural ensembles. First, we review non-recursive approaches and recursive point process filters, paying special attention to clusterless decoding strategies. We also discuss emerging approaches such as neural manifolds within Bayesian estimation. Next, we discuss the advanced application of Bayesian decoding in understanding the neuronal coding mechanisms of memory consolidation and planning, and in supporting computational model establishment and closed-loop manipulation. Finally, we discuss the limitations and challenges of recent approaches, highlighting the promising strategies that could raise the decoding efficiency and adapt the growing scale of neural data. We believe that the developing of Bayesian decoding approach would significantly benefit for techniques and applications of memory-related brain machine interface.},
}

@article {pmid41005325,
year = {2025},
author = {Botero, JP and Roberts, SM and Mackowiak, P and Witham, N and Selzer, L and Srikanthan, B and Zoschke, K and Negi, S and Solzbacher, F},
title = {Neuralace: manufacture, parylene-C coating, and mechanical properties.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0c39},
pmid = {41005325},
issn = {1741-2552},
abstract = {OBJECTIVE: This study investigates the mechanical properties of the Neuralace, a novel ultra-thin, high-channel-count mesh-type subdural electrode array, to characterize its mechanical compatibility with neural tissue (i.e., the forces exerted onto the brain upon conformation) for chronic brain-computer interface (BCI) applications.

APPROACH: A full-factorial design of experiments was used to assess the effects of geometrical variations, orientation, and polymeric encapsulation on the stiffness of silicon-based Neuralace structures. A custom low-force four-point bending setup was developed to measure flexural stiffness in a physiologically relevant displacement range.

MAIN RESULTS: The stiffness values of Neuralace structures ranged from 2.99 N/m to 7.21 N/m, depending on the cell-wall thickness (CWT) of the lace, orientation, and parylene-C (PPXC) encapsulation. Orientation and CWT had the largest impact on the stiffness of the structures, while the effects of PPXC encapsulation were statistically significant but more subtle. The stiffest Neuralace configuration is expected to exert forces approximately 10 to 100 times lower than commercially available subdural implants would when conforming to the brain's topology (considering a gyrus of 60 mm radius).

SIGNIFICANCE: Subdural electrode arrays have traditionally been used for epilepsy monitoring and surgical planning. These arrays are now transitioning from short-term implantation in epilepsy monitoring to long-term use in BCIs, which requires consideration of the foreign body response to ensure long-term durability and functionality. Biocompatibility challenges, such as fibrotic encapsulation and reactive astrogliosis, highlight the need for conformal subdural implant designs that minimize mechanical stress on neural tissue. This study establishes a rigorous and reproducible framework for mechanical characterization of conformable neural implants and demonstrates the feasibility of tuning design parameters to reduce implant-induced mechanical stress on cortical tissue. The results support future development of chronic BCI-compatible subdural electrodes with improved biocompatibility through mechanical design. .},
}

@article {pmid41005322,
year = {2025},
author = {Yue, J and Xiao, X and Zhang, H and Xu, M and Ming, D},
title = {BGTransform: a neurophysiologically informed EEG data augmentation framework.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0c3a},
pmid = {41005322},
issn = {1741-2552},
abstract = {Deep learning has emerged as a powerful approach for decoding electroencephalography (EEG)-based brain-computer interface (BCI) signals. However, its effectiveness is often limited by the scarcity and variability of available training data. Existing data augmentation methods often introduce signal distortions or lack physiological validity. This study proposes a novel augmentation strategy designed to improve generalization while preserving the underlying neurophysiological structure of EEG signals. Approach. We propose Background EEG Transform (BGTransform), a principled data augmentation framework that leverages the neurophysiological dissociation between task-related activity and ongoing background EEG. In contrast to existing methods, BGTransform generates new trials by selectively perturbing the background EEG component while preserving the task-related signal, thus enabling controlled variability without compromising class-discriminative features. We applied BGTransform to three publicly available EEG-BCI datasets spanning steady-state visual evoked potential (SSVEP) and P300 paradigms. The effectiveness of BGTransform is evaluated using several widely adopted neural decoding models under three training regimes: (1) without augmentation (baseline model), (2) with conventional augmentation methods, and (3) with BGTransform. Main Results. Across all datasets and model architectures, BGTransform consistently outperformed both baseline models and conventional augmentation techniques. Compared to models trained without BGTransform, it achieved average classification accuracy improvements of 2.45\%-15.52\%, 4.36-17.15\%, and 7.55-10.47\% across the three datasets, respectively. In addition, BGTransform demonstrated greater robustness across subjects and tasks, maintaining stable performance under varying recording conditions. Significance. BGTransform provides a principled and effective approach to augmenting EEG data, informed by neurophysiological insight. By preserving task-related components and introducing controlled variability, the method addresses the challenge of data sparsity in EEG-BCI training. These findings support the utility of BGTransform for improving the accuracy, robustness, and generalizability of deep learning models in neural engineering applications.},
}

@article {pmid41005320,
year = {2025},
author = {Guney, OB and Kucukahmetler, D and Ozkan, H},
title = {Source-free domain adaptation for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0c3d},
pmid = {41005320},
issn = {1741-2552},
abstract = {OBJECTIVE: SSVEP-based BCI spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments) to the new user (target domain), based only on the unlabeled target data.

APPROACH: Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances.

MAIN RESULTS: Our method achieves excellent 201.15 bits/min and 145.02 bits/min ITRs on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available at https://github.com/osmanberke/SFDA-SSVEP-BCI Significance: The proposed method priorities user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.},
}

@article {pmid41004906,
year = {2025},
author = {Cai, S and Lin, Z and Liu, X and Wei, W and Wang, S and Zhang, M and Schultz, T and Li, H},
title = {Spiking neural networks for EEG signal analysis: From theory to practice.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108127},
doi = {10.1016/j.neunet.2025.108127},
pmid = {41004906},
issn = {1879-2782},
abstract = {The intricate and efficient information processing of the human brain, driven by spiking neural interactions, has led to the development of spiking neural networks (SNNs) as a cutting-edge neural network paradigm. Unlike traditional artificial neural networks (ANNs) that use continuous values, SNNs emulate the brain's spiking mechanisms, offering enhanced temporal information processing and computational efficiency. This review addresses the critical gap between theoretical advancements and practical applications of SNNs in EEG signal analysis. We provide a comprehensive examination of recent SNN methodologies and their application to EEG signals, highlighting their potential benefits over conventional deep learning approaches. The review encompasses foundational knowledge of SNNs, detailed implementation strategies for EEG analysis, and challenges inherent to SNN-based methods. Practical guidance is provided through step-by-step instructions and accessible code available on GitHub, aimed at facilitating researchers' adoption of these techniques. Additionally, we explore emerging trends and future research directions, emphasizing the potential of SNNs to advance brain-computer interfaces and neurofeedback systems. This paper serves as a valuable resource for bridging the gap between theoretical developments in SNNs and their practical implementation in EEG signal analysis.},
}

@article {pmid41004593,
year = {2025},
author = {Zhang, L and Wang, S and Xia, J and Li, B and Zhang, S and Luo, J and Zhang, F and Zheng, T and Pan, G and Hasan, T and Yu, Y and Ding, G and Jin, H and Yang, Z and Dong, S},
title = {Monolithic multimodal neural probes for sustained stimulation and long-term neural recording.},
journal = {Science advances},
volume = {11},
number = {39},
pages = {eadu1753},
doi = {10.1126/sciadv.adu1753},
pmid = {41004593},
issn = {2375-2548},
mesh = {*Electrodes, Implanted ; Animals ; *Neurons/physiology ; Optical Fibers ; Biocompatible Materials/chemistry ; Electric Stimulation ; },
abstract = {Long-term implantable neural probes with dual-mode optical stimulation and simultaneous electrical recording are crucial for modulating neural loop activity in vivo. Traditional probes using "add-on" strategies often suffer from mechanical rigidity, compromised electrical performance, and insufficient biocompatibility, limiting their clinical applicability. In this study, we present a method for the direct laser writing of electrode arrays onto the curved surface of optical fibers, integrating them within a biocompatible polymer coating to create monolithic neural probes. The monolithic probes demonstrate high mechanical bending endurance, stable impedance, and improved biocompatibility, resulting in a lower inflammatory response compared to conventional systems. Furthermore, our method facilitates the multilayer integration of multilayer electrodes onto optical fibers, enabling high-density electrical readout channels. This advancement represents substantial progress in neuroengineering, with promising implications for future neural monitoring and modulation applications.},
}

*Electrodes, Implanted
Animals
*Neurons/physiology
Optical Fibers
Biocompatible Materials/chemistry
Electric Stimulation

@article {pmid41003965,
year = {2025},
author = {Shao, X and Chang, C and Wang, H},
title = {Impact of fatigue levels on EEG-based personal recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41003965},
issn = {1741-0444},
support = {92270113//National Natural Science Foundation of China/ ; 62176054//National Natural Science Foundation of China/ ; },
abstract = {The uniqueness of the electroencephalogram (EEG), a distinct biometric marker inherent to each individual, yields significant advantages for user authentication and identification in brain-computer interface (BCI) systems. However, EEG features can easily change according to the user's state, which may affect the performance of biometric recognition systems based on EEG. Notably, in EEG data collection for such systems, fatigue levels can fluctuate over time-a factor that has yet to be thoroughly investigated concerning its impact on individual recognition performance. This study explores the implications of fatigue on EEG-based personal recognition systems. We derived six sub-datasets from two simulated driving datasets, each labeled with varying levels of fatigue. From each sub-dataset, we extracted six features for identity recognition within and across different fatigue levels. Single-session and cross-session studies revealed that the disparity of EEG fatigue levels between the training and testing sets increased, and system recognition accuracy experienced a decline. Specifically, recognition accuracy typically fell by over 30 % after 90 min of simulated driving. Furthermore, identity recognition results are better when the training set includes EEG in more fatigued states compared to the test set, rather than the other way around. Crucially, the method based on functional connectivity features shows the best recognition accuracy under different fatigue levels. This research emphasizes the potential benefits of considering fatigue variations in EEG-based personal recognition systems.},
}

@article {pmid41003117,
year = {2025},
author = {Bizzarri, FP and Campetella, M and Recupero, SM and Bellavia, F and D'Amico, L and Rossi, F and Gavi, F and Filomena, GB and Russo, P and Palermo, G and Foschi, N and Totaro, A and Ragonese, M and Sighinolfi, MC and Racioppi, M and Sacco, E and Rocco, B},
title = {Female Sexual Function After Radical Treatment for MIBC: A Systematic Review.},
journal = {Journal of personalized medicine},
volume = {15},
number = {9},
pages = {},
doi = {10.3390/jpm15090415},
pmid = {41003117},
issn = {2075-4426},
abstract = {Background: Sexuality in women with muscle-invasive bladder cancer (MIBC) undergoing radical treatment represents a crucial aspect of their overall quality of life, which is increasingly recognized as a key component of patient-centered care and long-term well-being. This review aimed to analyze the available literature to provide a comprehensive overview of the effects of treatments on female sexual function. Methods: We included all qualitative and quantitative studies addressing sexual function in patients treated for MIBC. Excluded were narrative reviews, case reports, conference abstracts, systematic reviews, and meta-analyses. The included studies involved women undergoing either robot-assisted radical cystectomy (RARC) or open RC (ORC), often with nerve-sparing, vaginal-sparing, or pelvic organ-preserving techniques. Data on oncological and functional outcomes were collected. Results: A systematic review of 29 studies including 1755 women was conducted. RC was performed via robotic/laparoscopic approaches in 39% of cases and open techniques in 61%. Urinary diversions included orthotopic neobladders (48%), ileal conduits (42%), ureterocutaneostomies (3%), and Indiana pouches (7%). Radiotherapy, used in 6% of patients, was mainly applied in a curative, trimodal setting. Sexual function was evaluated using various pre- and/or postoperative questionnaires, most commonly the EORTC QLQ-C22, FACT-BL, Bladder Cancer Index (BCI), LENT SOMA, and Female Sexual Function Index (FSFI). Radiotherapy was associated with reduced sexual function, though outcomes were somewhat better than with surgery. Among surgical approaches, no differences in sexual outcomes were observed. Conclusions: Further qualitative research is essential to better understand the experience of FSD after treatment. Incorporating both patient and clinician perspectives will be key to developing tailored interventions. In addition, efforts should be made to standardize the questionnaires used to assess female sexual dysfunction, in order to improve comparability across studies and ensure consistent evaluation.},
}

@article {pmid41002025,
year = {2025},
author = {Tang, H and He, S and Tao, J and Wang, C and Wang, Z and Song, J},
title = {Mechanically Tunable Electromagnetic Metamaterials Based on Chains of Tension-rotation Coupling Units with Exceptional Reconfiguration Capability.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01423},
doi = {10.1002/smtd.202501423},
pmid = {41002025},
issn = {2366-9608},
support = {12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; GZC20232293//Postdoctoral Fellowship Program of CPSF/ ; 2022M710126//China Postdoctoral Science Foundation/ ; 2023M743011//China Postdoctoral Science Foundation/ ; BX20220268//China National Postdoctoral Program for Innovative Talents/ ; },
abstract = {Controlling the out-of-plane rotation of split-ring resonators (SRRs) represents an effective strategy to realize mechanically tunable electromagnetic (EM) materials. However, designing structures that can achieve substantial angular rotations via straightforward stretching operations while keeping the resonators intact remains a challenge. Here, a mechanically tunable EM metamaterial constructed from parallel chains of tension-rotation units that enable substantial out-of-plane rigid rotations exceeding 120° of the SRRs through simple stretch is reported. Theoretical, numerical, and experimental studies are conducted to reveal the deformation mechanism and quantify the relationship between tensile strain and rotation angles of SRRs. Comprehensive experimental and numerical studies show that the proposed metamaterial can extensively modulate the transmissions of both linearly and circularly polarized waves. Specifically, the transmission of TE wave exhibits a distinctive two-stage increasing-decreasing behavior, and the CD presents a unique zero-positive-zero-negative profile during stretching, which are not easily accessible by existing mechanically tunable EM metamaterials due to their limited deformation capabilities. Moreover, structural reconfiguration of chain arrangements enables tunable resonance frequencies while maintaining the frequency position of maximum CD, demonstrating robust preservation of the dominant chiral eigenmode. This study provides a valuable design strategy for developing mechanically tunable EM metamaterials with high tunability and multifunctionality.},
}

@article {pmid41000855,
year = {2025},
author = {Marin-Llobet, A and Lin, Z and Baek, J and Aljovic, A and Zhang, X and Lee, AJ and Wang, W and Lee, J and Shen, H and He, Y and Li, N and Liu, J},
title = {An AI Agent for cell-type specific brain computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.11.675660},
pmid = {41000855},
issn = {2692-8205},
abstract = {Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings-where direct ground-truth labeling is inherently unavailable-BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.},
}

@article {pmid40999875,
year = {2025},
author = {Balendra, and Sharma, N and Sharma, S},
title = {Transformed wavelets for motor imagery EEG classification using hybrid CNN-modified vision transformer: an exploratory study of MI EEG.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-18},
doi = {10.1080/10255842.2025.2563351},
pmid = {40999875},
issn = {1476-8259},
abstract = {Wavelets capture signal characteristics across time and frequency, but traditional wavelets suffer from high time-bandwidth products (TBP), limiting feature discrimination in EEG classification. We propose transformed wavelets with improved TBP and frequency bandwidth, outperforming Morlet by 0.04 and 0.20, respectively. Using datasets BCI Competition IV 2a, 2b, and CLA, we evaluated both fundamental and transformed wavelets with a modified vision transformer (MViT). Enhanced scalograms generated through local mean and principal component analysis (PCA) consistently outperformed raw scalograms. A hybrid convolutional neural network (CNN)-MViT achieved 82.35% inter-subject and 89.02% intra-subject accuracy, with 3-4% average gains in motor imagery EEG decoding.},
}

@article {pmid40999234,
year = {2025},
author = {Cai, C and Gao, L and Zhu, Z and Chen, W and Zhang, F and Yu, C and Xu, K and Zhu, J and Wu, H},
title = {Change in brain molecular landscapes following electrical stimulation of the nucleus accumbens.},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
volume = {},
number = {},
pages = {},
pmid = {40999234},
issn = {1740-634X},
support = {82401781//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82171519//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Deep brain stimulation (DBS) targeting the nucleus accumbens (NAc) is a promising therapeutic intervention for treatment-resistant neuropsychiatric disorders such as depression, anxiety, and addiction. However, the molecular mechanisms underlying the clinical efficacy of NAc DBS remain largely unknown. One approach to address this question is by performing spatial gene expression analysis on cells located in different regions of the same circuit following NAc DBS. In this study, we utilized high-resolution spatial transcriptomics (Stereo-seq) to investigate gene expression changes induced by NAc DBS in the mouse brain. Mice were randomly allocated to receive continuous electrical stimulation (0.1 mA, 130 Hz) or sham treatment (electrode implanted, no electrical stimulation given) for one week, and subsequent Stereo-seq analysis identified differentially expressed genes (DEGs) across various brain regions. Functional enrichment analysis highlighted changes in synaptic and neuroplasticity processes as well as stress and inflammatory responses in the NAc circuit. Single-cell resolution mapping further identified key molecular players, including Nlgn1, Snca, Pde10a, and Syt1, particularly in glutamate receptor-expressing neurons in the NAc. These genes are critical for synaptic plasticity and neurotransmitter release, and have been implicated in various psychiatric disorders. These findings shed light on the molecular underpinnings of NAc DBS and provide insights into its therapeutic potential in modulating neural circuits associated with neuropsychiatric disorders.},
}

@article {pmid40998792,
year = {2025},
author = {Chen, G and Zhang, X and Hu, X and Liu, Y and Yang, Y and Wang, W},
title = {Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8389},
pmid = {40998792},
issn = {2041-1723},
abstract = {Chemical reaction data is a pivotal asset, driving advances in competitive fields such as pharmaceuticals, materials science, and industrial chemistry. Its proprietary nature renders it sensitive, as it often includes confidential insights and competitive advantages organizations strive to protect. However, in contrast to this need for confidentiality, the current standard training paradigm for machine learning-based retrosynthesis gathers reaction data from multiple sources into one single edge to train prediction models. This paradigm poses considerable privacy risks as it necessitates broad data availability across organizational boundaries and frequent data transmission between entities, potentially exposing proprietary information to unauthorized access or interception during storage and transfer. In the present study, we introduce the chemical knowledge-informed framework (CKIF), a privacy-preserving approach for learning retrosynthesis models. CKIF enables distributed training across multiple chemical organizations without compromising the confidentiality of proprietary reaction data. Instead of gathering raw reaction data, CKIF learns retrosynthesis models through iterative, chemical knowledge-informed aggregation of model parameters. In particular, the chemical properties of predicted reactants are leveraged to quantitatively assess the observable behaviors of individual models, which in turn determines the adaptive weights used for model aggregation. On a variety of reaction datasets, CKIF outperforms several strong baselines by a clear margin.},
}

@article {pmid40997885,
year = {2025},
author = {Rouse, T and Lupkin, SM and McGinty, VB},
title = {Using economic value signals from primate prefrontal cortex in neuro-engineering applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0bf6},
pmid = {40997885},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-machine interface research has shown the efficacy of using motor and sensory-related neural signals to assist physically impaired patients. Despite the comparable ability to extract more abstract cognitive signals from the brain, little effort has been devoted to leveraging these signals in neuroengineering applications. In this study, we explore the use of neural signals related to economic value, a key cognitive construct, in a BMI context.

APPROACH: Using multivariate time series data collected from the orbitofrontal cortex in non-human primates, we develop deep learning-based neural decoders to predict the monkey's choice in a value-based decision-making task. We implement a reinforcement learning-based training approach to develop adaptive decoders that can be extended to handle multi-step decisions, which frequently arise in real-world settings.

MAIN RESULTS: We develop neural decoders leveraging subjective value signals to predict the monkey's choice with < 70% accuracy on average, with above-chance accuracy even when choice options are objectively equal. We show that this same decoder architecture can be trained to execute choice-related actions and execute action sequences aligned with the user's goal. Finally, we explore a decoder architecture that uses a neural forecasting model equipped with task-related information, and show that it makes high accuracy predictions ∼ 300 ms sooner than would otherwise be possible.

SIGNIFICANCE: These findings support the feasibility of user preference-informed neuroengineering devices that leverage abstract cognitive signals to aid users in goal-directed behavior. It demonstrates that using abstract cognitive signals in real-world settings may be more accurate when combined with information from multiple sources, such as motor and sensory regions. This research also highlights the potential need for systems to measure their confidence in their actions when user input is minimal.},
}

@article {pmid40997041,
year = {2025},
author = {Lee, Y and Chen, R and Bhattacharyya, S},
title = {An Online Learning Framework for Neural Decoding in Embedded Neuromodulation Systems.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {0},
doi = {10.1177/21580014251374627},
pmid = {40997041},
issn = {2158-0022},
abstract = {Introduction: Advancements in brain-computer interfaces (BCIs) have improved real-time neural signal decoding, enabling adaptive closed-loop neuromodulation. These systems dynamically adjust stimulation parameters based on neural biomarkers, enhancing treatment precision and adaptability. However, existing neuromodulation frameworks often depend on high-power computational platforms, limiting their feasibility for portable, real-time applications. Methods: We propose RONDO (Recursive Online Neural DecOding), a resource-efficient neural decoding framework that employs dynamic updating schemes in online learning with recurrent neural networks (RNNs). RONDO supports simple RNNs, long short-term memory networks, and gated recurrent units, allowing flexible adaptation to different signal type, accuracy, and real-time constraints. Results: Experimental results show that RONDO's adaptive model updating improves neural decoding accuracy by 35% to 45% compared to offline learning. Additionally, RONDO operates within real-time constraints of neuroimaging devices without requiring cloud-based or high-performance computing. Its dynamic updating scheme ensures high accuracy with minimal updates, improving energy efficiency and robustness in resource-limited settings. Conclusions: RONDO presents a scalable, adaptive, and energy-efficient solution for real-time closed-loop neuromodulation, eliminating reliance on cloud computing. Its flexibility makes it a promising tool for clinical and research applications, advancing personalized neurostimulation and adaptive BCIs.},
}

@article {pmid40996498,
year = {2025},
author = {Zhang, M and Zhang, Y and Liu, W and Sun, S and Xu, G},
title = {Quantifying and evaluating motor imagery ability using EEG microstates in MI-BCI training.},
journal = {Experimental brain research},
volume = {243},
number = {10},
pages = {216},
pmid = {40996498},
issn = {1432-1106},
support = {2022YFC2402200//the Nationnal Key R&D Program of China/ ; 2022YFC2402200//the Nationnal Key R&D Program of China/ ; 2022YFC2402200//the Nationnal Key R&D Program of China/ ; 2022YFC2402200//the Nationnal Key R&D Program of China/ ; 2022YFC2402200//the Nationnal Key R&D Program of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; 52320105008//the National Natural Science Foundation of China/ ; },
}

@article {pmid40995804,
year = {2025},
author = {Kim, E and Chung, WG and Kim, E and Oh, M and Paek, J and Lee, T and Kim, D and An, SH and Kim, S and Park, JU},
title = {Multi-Channel Neural Interface for Neural Recording and Neuromodulation.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01227},
doi = {10.1002/smtd.202501227},
pmid = {40995804},
issn = {2366-9608},
support = {//Ministry of Science & ICT (MSIT)/ ; //Ministry of Trade, Industry and Energy/ ; 2023R1A2C2006257//National Research Foundation/ ; RS-2024-00464032//National Research Foundation/ ; RS-2025-16063568//National Research Foundation/ ; RS-2024-00460364//STEAM Research Programs/ ; RS-2024-00406240//ERC Program/ ; 2E33191//Korea Institute of Science and Technology/ ; 2E33190//Korea Institute of Science and Technology/ ; RS-2025-00514998//Sejong Science Fellowship/ ; IBS-R026-D1//Institute for Basic Science/ ; },
abstract = {Neural interfaces have emerged as pivotal platforms for advancing digital neurotherapies by enabling the real-time acquisition and monitoring of neural signals. Traditional single-channel systems are inherently limited in their capacity to capture the complex and large-scale interactions among diverse neuronal populations. In contrast, multi-channel systems provide the high spatiotemporal resolution necessary to decode the dynamic activity of neural circuits across multiple brain and spinal cord regions. This review provides a comprehensive overview of recent advances in multi-channel neural interface technologies, encompassing both penetrating and non-penetrating systems for high-resolution electrophysiological recording, as well as multifunctional platforms that integrate additional modalities such as drug delivery, optical stimulation, and chemical sensing. Recent progress in this field has been driven by advances in structural and material design, including the development of soft, flexible architectures and materials for both substrates and electrodes, which improve long-term stability and minimize tissue damage. In parallel, emerging data analysis techniques have enhanced the capacity to decode complex neural activity patterns from high-dimensional, multi-channel recordings. These technological advancements have broadened the potential applications of neural interfaces in brain-machine interfaces (BMIs), facilitating precise neuromodulation, real-time monitoring of neurological states, and integration with immersive systems such as virtual and augmented reality.},
}

@article {pmid40995145,
year = {2025},
author = {Zabolotniy, A and Chan, RW and Moiseeva, V and Fedele, T},
title = {Convolutional neural networks decode finger movements in motor sequence learning from MEG data.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1623380},
pmid = {40995145},
issn = {1662-4548},
abstract = {OBJECTIVE: Non-invasive Brain-Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings.

APPROACH: We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response Convolutional Neural Network (LF-CNN). We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.

RESULTS: Sequence learning was reflected by a decrease in reaction times during SRTT performance. Movement laterality was decoded with an accuracy superior to 95% by all approaches, while for individual finger movement, decoding was in the 80-85% range. LF-CNN stood out for (1) its low computational time and (2) its interpretability in both spatial and spectral domains, allowing to examine neurophysiological patterns reflecting task-related motor cortex activity.

SIGNIFICANCE: We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience.},
}

@article {pmid40995144,
year = {2025},
author = {Citarella, J and Siekierski, P and Ethridge, L and Westerkamp, G and Liu, Y and Blank, E and Voorhees, L and Batterink, L and Jones, SR and Smith, E and Reisinger, DL and Nelson, M and Binder, DK and Razak, KA and Miyakoshi, M and Wu, S and Gilbert, D and Horn, PS and De Stefano, LA and Erickson, CA and Pedapati, EV},
title = {FX ENTRAIN: scientific context, study design, and biomarker driven brain-computer interfaces in neurodevelopmental conditions.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1618804},
pmid = {40995144},
issn = {1662-4548},
abstract = {Fragile X Syndrome (FXS), caused by the loss of function of the Fmr1 gene, is characterized by varying degrees of intellectual disability, autistic features, and sensory hypersensitivity. Despite phenotypic rescue in animal deletion models, clinical trials in humans have been unsuccessful, likely due to the heterogeneous nature of FXS. To uncover the basis of individual- and subgroup-level variation driving treatment failures, we propose to test and modulate thalamocortical drive as a novel "bottom-up" neural probe to understand the mechanics of FXS-relevant circuits. Our study employs trial-level EEG analyses (neurodynamics) to detect fine-grained differences in brain activity using sensory and statistical learning paradigms in children with FXS, autism spectrum disorder (ASD), and typically developing controls. Parallel analysis in the FXS knockout mouse model will clarify its relevance to human FXS subgroups. In a randomized crossover study, we will evaluate the efficacy of closed-loop auditory entrainment, indexed on individual neurodynamic measures, aiming to normalize neural responses and enhance statistical learning performance. We anticipate this approach will yield opportunities to identify more effective early interventions that alter the trajectory of intellectual development in FXS.},
}

@article {pmid40993190,
year = {2025},
author = {Merk, T and Köhler, RM and Brotons, TM and Vossberg, SR and Peterson, V and Lyra, LF and Vanhoecke, J and Chikermane, M and Binns, TS and Li, N and Walton, A and Neudorfer, C and Bush, A and Sisterson, N and Busch, J and Lofredi, R and Habets, J and Huebl, J and Zhu, G and Yin, Z and Zhao, B and Merkl, A and Bajbouj, M and Krause, P and Faust, K and Schneider, GH and Horn, A and Zhang, J and Kühn, AA and Mark Richardson, R and Neumann, WJ},
title = {Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40993190},
issn = {2157-846X},
support = {R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; R01NS110424//Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; 424778381//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01NS110424//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; R01 13478451//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 1R01NS127892-01//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; UM1NS132358//U.S. Department of Health Human Services | National Institutes of Health (NIH)/ ; 101077060//European Commission (EC)/ ; },
abstract = {Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.},
}

@article {pmid40990260,
year = {2025},
author = {Lebani, BR and da Silva, AB and Silva, LT and Girotti, ME and Pinto, ER and Skaff, M and Almeida, FG},
title = {Is It Necessary to Remove the Maximum Prostate Tissue in All Patients? the Percentage of Resected Prostate Tissue and the Influence on Surgery Outcomes: A One-Year Follow Up Study.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70152},
pmid = {40990260},
issn = {1520-6777},
support = {//The study developed includes only patients treated inside the Brazilian public health system. There were not any additional costs involved./ ; },
abstract = {INTRODUCTION: To investigate whether the volume of the prostate tissue resected on TURP influences on short and medium term follow up.

METHODS: It was developed a prospective study between May 2020 and August 2022, embracing patients with severe LUTS due to BPO, including clinical and urodynamic parameters meeting obstruction criteria (BOOI > 40), and good detrusor function (BCI > 100). Patients were assessed at 1, 6 and 12 months follow up. The primary endpoint was to compare whether the amount of resected tissue after TURP influences uroflowmetry at 12 months follow up (Qmax, ml/sec). The secondary endpoint was to compare different percentages of resected tissue (RPT) and its relation to the outcomes.

RESULTS: Ninety-six patients with mean age of 70,4 ± 7.96 years. At baseline, prostate volume was 78.5 ± 51.8 cc³, Qmax was 6.03 ± 3.09 ml/sec and post void residual (PVR) was 113 ± 132 ml, IPSS of 24.9 ± 6.75. All of them were urodinamically obstructed (BOOI 86.7 ± 35.6) and good detrusor function (BCI 130 ± 28.6). The general RPT was 45.5 ± 27.7%. The higher the RTP, the lower the PSA at 1 month follow up (p < 0.001, R = 0.521). Nevertheless, it was not found correlation between the RTP and Qmax, IPSS or PVR.

CONCLUSION: TURP improves clinical and urodynamic parameters at 1 year follow up, independent of the amount of resected prostate tissue, in patients with bladder outlet obstruction and good detrusor function, since the surgery is effective.},
}

@article {pmid40990135,
year = {2025},
author = {Jin, J and Wang, Z and Dai, L and Wang, A and Gao, L},
title = {An Exploratory Study of Loss Averse in Group Decision Contexts: Multiple Pieces of Evidence From ERPs and Machine Learning.},
journal = {Psychophysiology},
volume = {62},
number = {9},
pages = {e70155},
doi = {10.1111/psyp.70155},
pmid = {40990135},
issn = {1469-8986},
support = {72271166//National Natural Science Foundation of China/ ; 72501175//National Natural Science Foundation of China/ ; 22dz2261100//Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 2024DSYL051//Tutor Academic Leadership Program of shanghai international Studies University/ ; },
mesh = {Humans ; *Machine Learning ; Electroencephalography ; Male ; Female ; Young Adult ; *Decision Making/physiology ; Adult ; *Evoked Potentials/physiology ; *Group Processes ; Adolescent ; *Risk-Taking ; *Feedback, Psychological/physiology ; },
abstract = {Both laboratory and field evidence have shown differences in risk attitudes between individual and group decision contexts. Loss aversion, a crucial aspect of risk attitudes, whose behavioral performance and neural mechanism in group decision contexts remain unclear, differs from other risk attitudes such as risk aversion. Using behavioral and electroencephalography (EEG) experiments with non-student and student samples, we conducted an exploratory study to examine the behavioral performance and neural mechanisms of loss aversion in group decision contexts. Behaviorally, we found a reduction effect of loss aversion in group decision contexts compared to individual decision contexts. ERP results from the average and single-trial analyses jointly found that individuals are less sensitive to losses and gains in group (vs. individual) decision contexts, as evidenced by the vanishing Feedback-related Negativity (FRN) and P3b differences to losses and gains. We also found a significant negative correlation between the loss aversion coefficient and FRN amplitude induced by losses both in individual and group decision contexts, which indicated the relationship between loss aversion and neural signals that process loss outcomes. Furthermore, machine learning analyses revealed that EEG features exhibit a high accuracy rate of 81.25% in predicting the decision contexts. This finding underscores the intricate relationship between neural activity and loss aversion across varying decision contexts, highlighting the potential of neurophysiological activity to elucidate the underlying cognitive processes involved in loss aversion. This paper advances our understanding of loss aversion in group decision contexts by providing multiple pieces of evidence for behavioral performance, neural activities, and machine learning. Findings can help to optimize group decision-making processes and resource allocation, and to reduce inefficiencies caused by irrational behavior and resistance to beneficial changes.},
}

Humans
*Machine Learning
Electroencephalography
Male
Female
Young Adult
*Decision Making/physiology
Adult
*Evoked Potentials/physiology
*Group Processes
Adolescent
*Risk-Taking
*Feedback, Psychological/physiology

@article {pmid40989443,
year = {2025},
author = {Su, K and Tian, L},
title = {Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.},
journal = {PeerJ. Computer science},
volume = {11},
number = {},
pages = {e2938},
pmid = {40989443},
issn = {2376-5992},
abstract = {This article systematically reviews the latest developments in electroencephalogram (EEG)-based speech imagery brain-computer interface (SI-BCI). It explores the brain connectivity of SI-BCI and reveals its key role in neural encoding and decoding. It analyzes the research progress on vowel-vowel and vowel-consonant combinations, as well as Chinese characters, words, and long-words speech imagery paradigms. In the neural encoding section, the preprocessing and feature extraction techniques for EEG signals are discussed in detail. The neural decoding section offers an in-depth analysis of the applications and performance of machine learning and deep learning algorithms. Finally, the challenges faced by current research are summarized, and future directions are outlined. The review highlights that future research should focus on brain region mechanisms, paradigms innovation, and the optimization of decoding algorithms to promote the practical application of SI-BCI technology.},
}

@article {pmid40988031,
year = {2025},
author = {Rab, P and Shirinskiy, IJ and Kimmeyer, M and Macken, AA and Calamita, AG and Colombini, AG and Buijze, GA and Lafosse, T},
title = {Augmentation of full-thickness rotator cuff tears with a bioinductive collagen implant does not reduce retear rates - a propensity matched cohort study.},
journal = {BMC musculoskeletal disorders},
volume = {26},
number = {1},
pages = {855},
pmid = {40988031},
issn = {1471-2474},
mesh = {Humans ; Female ; Male ; Middle Aged ; *Rotator Cuff Injuries/surgery/diagnostic imaging ; Retrospective Studies ; Aged ; *Collagen/administration & dosage ; Range of Motion, Articular ; Treatment Outcome ; Propensity Score ; Follow-Up Studies ; *Prostheses and Implants ; },
abstract = {PURPOSE: To compare the clinical and radiographic outcomes after full-thickness RC repair with and without performing augmentation with a bioinductive collagen implant (BCI).

MATERIALS AND METHODS: Consecutive patients who underwent primary repair of a full-thickness supraspinatus tear between 05/2021 and 11/2023 were retrospectively identified. Patients at elevated risk for retear were defined by biological, radiographic, and intraoperative risk factors. Those who underwent repair with or without concomitant augmentation using a BCI and who had both clinical and radiographic follow-up at 1 year postoperatively were matched in a 1:1 ratio according to age, sex, body mass index, tear size, smoking status, diabetes, and American Society of Anesthesiologists physical status classification. Range of motion (ROM) as well as patient-reported outcome measures (Auto-Constant-Score (CS), American Shoulder and Elbow Surgeons (ASES) Score, Subjective Shoulder Value (SSV), and Visual Analog Scale (VAS) for pain) were recorded. Magnetic resonance imaging performed at 1 year postoperatively was analyzed and the presence of retear was recorded.

RESULTS: In total, 149 patients with a radiographic and clinical follow-up at 1 year postoperatively were identified. Of these, 23 patients with BCI augmentation were matched to 23 patients without placement of BCI (48% female, 59.2 ± 8.4 years at surgery). A retear occurred in 5 patients (21.7%) in the BCI augmentation group and in 3 patients (13.0%) in the control group (p = 0.72). No significant difference was reported regarding the CS (77 [71-83] vs. 76 [63-81], p = 0.5), ASES Score (92 [82-98] vs. 90 [84-95], p = 0.8), SSV (90 [80-100] vs. 90 [88-95], p = 0.9), VAS for pain (p = 0.74), or ROM between the groups.

CONCLUSION: In this retrospective matched cohort of patients at elevated risk for retear, augmentation of full-thickness RC repair with a BCI was not associated with a reduced retear rate. Moreover, no significant differences regarding clinical and functional outcome were found between the two groups.

LEVEL OF EVIDENCE: III - Retrospective case series with a matched control group.},
}

Humans
Female
Male
Middle Aged
*Rotator Cuff Injuries/surgery/diagnostic imaging
Retrospective Studies
Aged
*Collagen/administration & dosage
Range of Motion, Articular
Treatment Outcome
Propensity Score
Follow-Up Studies
*Prostheses and Implants

@article {pmid40987603,
year = {2025},
author = {Rana, D and Babushkina, N and Gini, M and Flores Cáceres, A and Li, H and Maybeck, V and Criscuolo, V and Mayer, D and Ienca, M and Musall, S and Rincon Montes, V and Offenhäusser, A and Santoro, F},
title = {Neural vs Neuromorphic Interfaces: Where Are We Standing?.},
journal = {Chemical reviews},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.chemrev.4c00862},
pmid = {40987603},
issn = {1520-6890},
abstract = {Neuromorphic interfaces represent a transformative frontier in neural engineering, enabling seamless communication between the nervous system and external devices through biologically inspired computing architectures. These systems offer promising avenues for diagnosing and treating neurological disorders by emulating the brain's computational strategies. Neural devices, including sensors and stimulators, monitor or modulate neural activity, playing a pivotal role in deciphering brain function and neuropathologies. Yet, clinical translation remains limited due to persistent challenges such as foreign body responses, low signal-to-noise ratios, and constraints in real-time data processing. Recent breakthroughs in neuromorphic hardware, neural recording, and stimulation technologies are addressing these challenges, paving the way for more adaptive and efficient brain-machine interfaces and neuroprosthetics. This review highlights the emerging class of neurohybrid interfaces, where neuromorphic systems might be integrated to enhance bidirectional neural communication. It emphasizes novel material strategies engineered for seamless neural interfacing and their incorporation into advanced neuromorphic chip architectures capable of real-time signal processing and closed-loop feedback. Furthermore, this review explores cutting-edge neuromorphic biointerfaces and evaluates the technological, biological, and ethical challenges involved in their clinical deployment. By bridging materials science, neuroscience, and neuromorphic engineering, these systems hold the potential to redefine the landscape of neurotechnology.},
}

@article {pmid40988938,
year = {2024},
author = {Oganesian, LL and Shanechi, MM},
title = {Brain-computer interfaces for neuropsychiatric disorders.},
journal = {Nature reviews bioengineering},
volume = {2},
number = {8},
pages = {653-670},
pmid = {40988938},
issn = {2731-6092},
abstract = {Neuropsychiatric disorders such as major depression are a leading cause of disability worldwide with standard treatments, including psychotherapy or medication, failing many patients. Deep brain stimulation holds great potential as an alternative therapy for treatment-resistant cases; however, improving the efficacy of stimulation therapy for neuropsychiatric disorders is hindered by the complexity as well as inter- and/or intra-individual variability in symptom manifestations, neural representations and response to therapy. These challenges motivate the development of brain-computer interfaces (BCIs) that can decode a patient's symptom-state from brain activity as feedback to personalize the stimulation therapy in closed loop. Here, we review progress on developing BCIs for neuropsychiatric care, focusing on neural biomarkers for decoding symptom-states, stimulation site selection and closed-loop stimulation strategies. Moreover, we highlight promising data-driven machine learning and system design approaches and provide a roadmap for realizing these BCIs. Finally, we review current limitations, discuss extensions to other treatment modalities, and outline the required scientific and technological advances. These advances can enable next-generation BCIs that provide an alternative therapy for treatment-resistant neuropsychiatric disorders.},
}

@article {pmid40984876,
year = {2025},
author = {Mishra, R and Agrawal, RK and Kirar, JS},
title = {Msst-eegnet: multi-scale spatio-temporal feature extraction using inception and temporal pyramid pooling for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {150},
pmid = {40984876},
issn = {1871-4080},
abstract = {Motor imagery classification is an essential component of Brain-computer interface systems to interpret and recognize brain signals generated during the visualization of motor imagery tasks by a subject. The objective of this work is to develop a novel DL model to extract discriminative features for better generalization performance to recognize motor imagery tasks. This paper presents a novel Multi-scale spatio-temporal network (MSST-EEGNet) to extract discriminative temporal, spectral, and spatial features for motor imagery task classification. The proposed MSST-EEGNet model includes three modules namely the inception module with dilated convolution, the temporal pyramid pooling module, and the classification module. Multi-scale temporal features along with spatial features are extracted using the inception block with the dilated convolution module. A set of multi-level fine-grained and coarse-grained features are extracted using a temporal pyramid pooling module. Further, categorical cross-entropy in combination with center loss is used as a loss function. Experiments are carried out on three benchmark datasets including the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset. The evaluation results shows that the proposed MSST-EEGNet model outperforms eight existing DL models in terms of classification accuracy for subject-specific and cross-session settings. It also outperforms eight existing DL models and six existing transfer-learning models for cross-subject setting. For the subject-specific classification the proposed MSST-EEGNet model achieved an accuracy of 0.8426 ± 0.1061, 0.7779 ± 0.0938, and 0.7365 ± 0.1477 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-session setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7709 ± 0.1098, 0.7524 ± 0.1017, and 0.6860 ± 0.0990 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. For the cross-subject setting, the proposed MSST-EEGNet model achieved an accuracy of 0.7288 ± 0.0730, 0.8161 ± 0.963, and 0.7075 ± 0.0746 on the BCI Competition IV-2a dataset, the BCI Competition IV-2b dataset, and the OpenBMI dataset respectively. Furthermore, a non-parametric Friedman statistical test demonstrates statistically significant superior performance of the proposed MSST-EEGNet model over the existing models.},
}

@article {pmid40983603,
year = {2025},
author = {Kim, G and Jeong, H and Kim, K and Lee, S and Baeg, E and Yang, S and Kim, B and Yang, S},
title = {The Pre-clincal Safety of Graphene-based Electrodes Implanted on Rat Cerebral Cortex.},
journal = {Experimental neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.5607/en25018},
pmid = {40983603},
issn = {1226-2560},
abstract = {Graphene has emerged as a promising nanomaterial for brain-computer interface (BCI) applications due to its excellent electrical properties and biocompatibility. However, its long-term structural compatibility on the cerebral cortex requires further validation. This study assessed both functional compatibility and preservation of neural tissue architecture for graphene/parylene C composite electrodes implanted on the rat cortical surface, in accordance with ISO 10993-6 guideline weekly neurobehavioral assessments and comprehensive histopathological analyses were conducted for four weeks post-implantation. Our results revealed no significant differences in neurobehavioral outcomes between graphene-based and medical-grade silicone implants. Histopathological examination showed no noticeable inflammatory responses, changes in cellular morphology, myelination status, or neuronal degeneration. These findings indicate that graphene electrodes preserve tissue integrity comparable to medical‑grade silicone. Our study supports graphene's potential use in clinical neuroprosthetics and neuromodulation devices.},
}

@article {pmid40983076,
year = {2025},
author = {He, Z and Wang, Y},
title = {TFDISNet：Temporal-Frequency Domain-Invariant and Domain-Specific Feature Learning Network for Enhanced Auditory Attention Decoding from EEG Signals.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae09b2},
pmid = {40983076},
issn = {2057-1976},
abstract = {Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integrate temporal and frequency domain information, resulting in constrained classification accuracy and robustness. To address these shortcomings, a novel framework, termed the Temporal-Frequency Domain-Invariant and Domain-Specific Feature Learning Network (TFDISNet), is proposed to enhance AAD performance. A dual-branch architecture is utilized to independently extract features from the temporal and frequency domains, which are subsequently fused through an advanced integration strategy. Within the fusion module, shared features, common across both domains, are aligned by minimizing a similarity loss, while domain-specific features, essential for the task, are preserved through the application of a dissimilarity loss. Additionally, a reconstruction loss is employed to ensure that the fused features accurately represent the original signal. These fused features are then subjected to classification, effectively capturing both shared and unique characteristics to improve the robustness and accuracy of AAD. Experimental results show TFDISNet outperforms state-of-the-art models, achieving 97.1% accuracy on the KUL dataset and 88.2% on the DTU dataset with a 2-second window, validated across group, subject-specific, and cross-subject analyses. Component studies confirm that integrating temporal and frequency features boosts performance, with the full TFDISNet surpassing its variants. Its dual-branch design and advanced loss functions establish a robust EEG-based AAD framework, setting a new field standard. .},
}

@article {pmid40982479,
year = {2025},
author = {Zhong, Y and Song, M and Shi, W and Di, S and Yu, C and Jiang, T},
title = {Robust population orientation encoding by orientation-untuned neurons in macaque V1.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {9},
pages = {},
doi = {10.1093/cercor/bhaf264},
pmid = {40982479},
issn = {1460-2199},
support = {2024M753502//China Postdoctoral Science Foundation/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024RC4028//Science and Technology Innovation Program of Hunan Province/ ; YJKYYQ20190040//Equipment Development Project of the Chinese Academy of Sciences/ ; 62403465//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; 62327805//National Natural Science Foundation of China/ ; 2021ZD0200200//Science and Technology Innovation (STI) 2030-Major Projects/ ; },
mesh = {Animals ; *Neurons/physiology ; *Orientation/physiology ; Photic Stimulation ; Macaca mulatta ; *Primary Visual Cortex/physiology/cytology ; Visual Pathways/physiology ; Male ; *Visual Perception/physiology ; *Visual Cortex/physiology ; *Orientation, Spatial/physiology ; Neural Networks, Computer ; Models, Neurological ; },
abstract = {Orientation is one of the most fundamental stimulus features in visual perception. In the primary visual cortex (V1), while most neurons are orientation-selective, a small portion exhibits a lack of this selectivity. However, it remains unclear what roles the orientation-untuned V1 neurons play in population orientation discrimination. Here, we analyzed data from a 2-photon calcium imaging study that recorded the responses of thousands of V1 neurons to a grating stimulus at various orientations in awake macaques. Our population analysis reveals that orientation-untuned neurons can independently decode stimulus orientation with accuracy comparable to tuned neurons. Remarkably, we found that the more critical role of orientation-untuned neuronal populations in orientation encoding is to enhance coding robustness, specifically by reducing sensitivity to noise. Moreover, when using artificial neural networks to model the primate ventral visual pathway, we found that the V1-like layer also contains a proportion of orientation-untuned units. Removing these units leads to significant impairments in natural object recognition. Overall, these results indicate that orientation-untuned neurons encode orientation information and play a crucial role in primate visual perception. The study provides compelling evidence for a continuous distribution of visual features across neurons and challenges the notion of highly specialized units.},
}

Animals
*Neurons/physiology
*Orientation/physiology
Photic Stimulation
Macaca mulatta
*Primary Visual Cortex/physiology/cytology
Visual Pathways/physiology
Male
*Visual Perception/physiology
*Visual Cortex/physiology
*Orientation, Spatial/physiology
Neural Networks, Computer
Models, Neurological

@article {pmid40982226,
year = {2025},
author = {Li, J and Yi, Y and Gao, X and Ren, Y and Gan, L and Zou, T and Qin, X and Tan, A and Yang, X and Jiang, F and Liu, X and Gao, H and Wang, Y and Aumont, E and Xiao, J and Zhou, B and Liao, W and Chen, H and Zhang, W and Montembeault, M and Rosa-Neto, P and Li, R},
title = {High brain network dynamics mediate audiovisual integration deficits and cognitive impairment in Alzheimer's disease.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251376717},
doi = {10.1177/13872877251376717},
pmid = {40982226},
issn = {1875-8908},
abstract = {BackgroundAudiovisual integration deficits are frequent in patients with Alzheimer's disease (AD). In addition, patients with AD have altered functional brain networks, such as those supporting auditory and visual processing. However, the mechanisms driving this association remain unclear.ObjectiveTo investigate whether dynamic functional network disruptions underlie audiovisual integration and cognitive deficits in AD.MethodsSeventy-nine participants (41 AD, 38 controls) completed audiovisual stimuli tasks. A multilayer modularity algorithm was utilized to assess the resting-state fMRI-based brain dynamics of the primary sensory and higher-order functional networks. Mediation analysis was conducted to test our hypothesis.ResultsAD patients showed delayed response time and reduced peak benefit of audiovisual integration. Dynamic switching rates of primary sensory and higher-order networks were significantly increased in AD, particularly in the dynamic integration between the default mode network (DMN) and visual network (VN). The peak benefit of audiovisual integration negatively correlated with DMN-VN dynamic integration and positively with Mini-Mental State Examination, Montreal Cognitive Assessment, and Auditory Verbal Learning Test delayed scores. Notably, excessive integration between the DMN and VN mediated the relationship between audiovisual integration deficits and cognitive impairment in patients with AD.ConclusionsThese findings suggest that audiovisual integration impairment may disturb the dynamic integration between the DMN and VN, contributing to cognitive impairment in AD. The neural mechanisms underlying audiovisual integration deficit and cognitive decline might help with early diagnosis and intervention for AD.},
}

@article {pmid40978101,
year = {2025},
author = {Liu, H and Liu, W and Du, Z and Wu, L and Chen, M and Gao, Z and Jiang, K and Li, L and Fan, Z and Shen, G},
title = {Encoding of blink information via wireless contact lens for eye-machine interaction.},
journal = {National science review},
volume = {12},
number = {10},
pages = {nwaf338},
pmid = {40978101},
issn = {2053-714X},
abstract = {Blinks controlled by ocular muscles and nerves can manifest as either involuntary physiological behaviors or volitional control actions, with the former serving spontaneous protective functions while the latter constitutes a biologically meaningful communicative signal. The encoding of blink information provides a novel eye-machine interaction (EMI) prototype within the realm of human-machine interaction, expanding human consciousness and capability boundaries. It facilitates motor and language rehabilitation, silent communication and even voluntary command execution. However, existing EMI devices face challenges related to wireless functionalities, ocular comfort and multi-route encoding/decoding orders. Here, we propose a wireless eye-wearable lens to encode conscious blink information via introduction of an RLC oscillating loop in the soft contact lens. The developed EMI contact lens incorporates a mechanosensitive capacitor, an inductive coil and the inherent loop resistance, generating characteristic resonance frequency for front-end capacitance signal transition or back-end control signal extraction. The EMI device delivers a sensitivity of 0.153 MHz/mmHg in the wide range of 0-70 mmHg for a normal intraocular pressure monitor and realizes conscious blink-based control command coding. A trial with participants having the EMI contact lens inserted demonstrates its wearability and biocompatibility. Finally, the five-route blink-based control command decoding mechanism is constructed via the EMI lens, linking blink counts to a drone's flight trajectory. The EMI contact lens offers an innovative prototype that transcends the capabilities of traditional brain-computer interfaces.},
}

@article {pmid40977080,
year = {2025},
author = {Li, W and Zou, H and Yang, B and Xiao, L and Liu, S and Chen, Z and Xie, L and Zhu, W and Zhao, X and Wang, L and Li, T and Wang, T},
title = {From Electrophysiological to Biochemically-Modulated Interfaces: Evolution of Brain-Machine Communication.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01471},
doi = {10.1002/smtd.202501471},
pmid = {40977080},
issn = {2366-9608},
support = {62235008//National Natural Science Foundation of China/ ; 62322108//Excellent Young Scholars of NSFC/ ; 62571260//General Program of NSFC/ ; 62201286//Young Scholars of NSFC/ ; 62301283//Young Scholars of NSFC/ ; 22405131//Young Scholars of NSFC/ ; 2023ZB587//Jiangsu Funding Program for Excellent Postdoctoral Talent/ ; NY222099//Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications/ ; BK20243057//Basic Research Program of Jiangsu Province/ ; },
abstract = {Brain-machine interfaces (BMIs) establish bidirectional communication between biological neural systems and external devices by decoding neural signals and delivering feedback stimulation. Achieving seamless integration with biological systems has driven the paradigmatic evolution of BMI technology through three interconnected dimensions. This review summarizes the shift from electrophysiological to biochemically-modulated BMIs, emphasizing key evolutionary trends that mirror biological neural characteristics. First, signal modalities have expanded from single electrophysiological detection to integrated biochemical sensing, enabling comprehensive neural circuit analysis through dual electrical-chemical communication pathways that capture both rapid electrical transmission and slower biochemical processes. Second, electrode morphology has transformed from rigid silicon structures to flexible, adaptive materials that mechanically match neural tissue properties, reducing mechanical mismatch and improving long-term biocompatibility. Third, system architectures have evolved from passive monitoring to active closed-loop platforms that incorporate neuromorphic intelligence and real-time therapeutic feedback, enabling dynamic neuromodulation based on multimodal signal analysis. Despite significant progress, challenges remain in achieving high electrode longevity, developing scalable multimodal interfaces, as well as understanding fundamental neural communication mechanisms. Future directions point toward biochemically-modulated brain interfaces incorporating living, adaptive, and evolutionarily responsive components that seamlessly integrate with biological neural networks for precision neurological therapeutics.},
}

@article {pmid40976830,
year = {2025},
author = {Chen, W and Xie, C and Wang, Y and Jin, Y and Zhao, Y and Xu, Y and Zhang, C and Chen, A and Wang, X and Jia, Z},
title = {Efficacy analysis of 450 nm semiconductor blue laser enucleation of the prostate in treating benign prostatic hyperplasia with urinary retention.},
journal = {Lasers in medical science},
volume = {40},
number = {1},
pages = {377},
pmid = {40976830},
issn = {1435-604X},
mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/complications ; *Urinary Retention/surgery/etiology ; Aged ; Retrospective Studies ; *Lasers, Semiconductor/therapeutic use ; Middle Aged ; Quality of Life ; Treatment Outcome ; Aged, 80 and over ; Urodynamics ; *Laser Therapy/methods ; *Prostatectomy/methods ; },
abstract = {To evaluate the clinical efficacy of 450 nm semiconductor blue laser enucleation of the prostate in patients with benign prostatic hyperplasia (BPH) complicated by acute urinary retention, and to assess its outcomes in patients with concomitant detrusor underactivity (DU).A retrospective analysis was conducted on clinical data from patients diagnosed with BPH and acute urinary retention who underwent 450 nm blue laser enucleation of the prostate in the Department of Urology at our hospital between February 2023 and May 2024. All patients had indwelling catheters due to acute urinary retention prior to surgery. Maximum urinary flow rate (Qmax), postvoid residual urine volume (PVR), International Prostate Symptom Score (IPSS), and quality of life (QoL) scores were compared before surgery and at 3 months postoperatively. Based on preoperative urodynamic testing, patients were divided into a DU group (bladder contractility index, BCI < 100) and a non-DU group (BCI ≥ 100). Surgical outcomes were compared between the two groups.A total of 62 patients were included in the study, with a mean age of 71.5 years. Of these, 32 (54.8%) were in the DU group and 28 (45.2%) in the non-DU group. At 3 months postoperatively, all patients showed significant improvements in Qmax, PVR, IPSS, and QoL scores compared with baseline (P < 0.001). In the DU group, 2 patients experienced recurrent urinary retention after catheter removal on postoperative day 3, but both recovered spontaneous urination after re-catheterization for 1 week. Intergroup comparisons showed that Qmax was lower and PVR was higher in the DU group than in the non-DU group at 3 months (P < 0.001), while no significant differences were observed in IPSS and QoL scores between the two groups (P > 0.05).The 450 nm semiconductor blue laser enucleation of the prostate is a safe and effective treatment for BPH complicated by acute urinary retention. Although patients with DU show less improvement in early postoperative voiding function compared to those without DU, the procedure effectively alleviates symptoms and may prevent further deterioration of detrusor function. These findings support its clinical application and wider adoption.},
}

Humans
Male
*Prostatic Hyperplasia/surgery/complications
*Urinary Retention/surgery/etiology
Aged
Retrospective Studies
*Lasers, Semiconductor/therapeutic use
Middle Aged
Quality of Life
Treatment Outcome
Aged, 80 and over
Urodynamics
*Laser Therapy/methods
*Prostatectomy/methods