@article {pmid39622169,
year = {2024},
author = {Wei, X and Narayan, J and Faisal, A},
title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9957},
pmid = {39622169},
issn = {1741-2552},
abstract = {Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has aided patients via brain-computer interfaces in neural activity analysis. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach. We introduce the Sandwich as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The Sandwich framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning, and individual classifiers (final layers) for specific tasks of each data set. It enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy. We evaluated the `Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models, our `Sandwich' implementations showed superior performance. The best-performing model, the Inception Sandwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%. The `Sandwich' framework demonstrates significant advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy as a model-agnostic meta-framework.},
}

@article {pmid39622162,
year = {2024},
author = {Chen, C and Fang, H and Yang, Y and Zhou, Y},
title = {Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9956},
pmid = {39622162},
issn = {1741-2552},
abstract = {Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy. Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable Electroencephalogram (EEG)-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition. Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures. Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces (BCIs).},
}

@article {pmid39621862,
year = {2024},
author = {Xia, Y and He, M and Basang, S and Sha, L and Huang, Z and Jin, L and Duan, Y and Tang, Y and Li, H and Lai, W and Chen, L},
title = {Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.},
journal = {JMIR medical informatics},
volume = {12},
number = {},
pages = {e57727},
doi = {10.2196/57727},
pmid = {39621862},
issn = {2291-9694},
mesh = {Humans ; *Electronic Health Records ; *Epilepsy/diagnosis/classification ; *Machine Learning ; Retrospective Studies ; *Natural Language Processing ; Female ; Male ; Adult ; Middle Aged ; China ; Adolescent ; Child ; Young Adult ; },
abstract = {BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.

OBJECTIVE: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.

METHODS: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.

RESULTS: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).

CONCLUSIONS: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.},
}

Humans
*Electronic Health Records
*Epilepsy/diagnosis/classification
*Machine Learning
Retrospective Studies
*Natural Language Processing
Female
Male
Adult
Middle Aged
China
Adolescent
Child
Young Adult

@article {pmid39621615,
year = {2024},
author = {Yuan, X and Li, H and Guo, F},
title = {Temperature cues are integrated in a flexible circadian neuropeptidergic feedback circuit to remodel sleep-wake patterns in flies.},
journal = {PLoS biology},
volume = {22},
number = {12},
pages = {e3002918},
doi = {10.1371/journal.pbio.3002918},
pmid = {39621615},
issn = {1545-7885},
mesh = {Animals ; *Sleep/physiology ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Connectome ; *Drosophila melanogaster/physiology ; *Neuropeptides/metabolism/genetics ; Neurons/physiology/metabolism ; Temperature ; Wakefulness/physiology ; Feedback, Physiological ; Brain/physiology/metabolism ; Drosophila/physiology ; Cues ; Signal Transduction ; },
abstract = {Organisms detect temperature signals through peripheral neurons, which relay them to central circadian networks to drive adaptive behaviors. Despite recent advances in Drosophila research, how circadian circuits integrate temperature cues with circadian signals to regulate sleep/wake patterns remains unclear. In this study, we used the FlyWire brain electron microscopy connectome to map neuronal connections, identifying lateral posterior neurons LPNs as key nodes for integrating temperature information into the circadian network. LPNs receive input from both circadian and temperature-sensing neurons, promoting sleep behavior. Through connectome analysis, genetic manipulation, and behavioral assays, we demonstrated that LPNs, downstream of thermo-sensitive anterior cells (ACs), suppress activity-promoting lateral dorsal neurons LNds via the AstC pathway, inducing sleep Disrupting LPN-LNd communication through either AstCR1 RNAi in LNds or in an AstCR1 mutant significantly impairs the heat-induced reduction in the evening activity peak. Conversely, optogenetic calcium imaging and behavioral assays revealed that cold-activated LNds subsequently stimulate LPNs through NPF-NPFR signaling, establishing a negative feedback loop. This feedback mechanism limits LNd activation to appropriate levels, thereby fine-tuning the evening peak increase at lower temperatures. In conclusion, our study constructed a comprehensive connectome centered on LPNs and identified a novel peptidergic circadian feedback circuit that coordinates temperature and circadian signals, offering new insights into the regulation of sleep patterns in Drosophila.},
}

Animals
*Sleep/physiology
*Circadian Rhythm/physiology
*Drosophila Proteins/metabolism/genetics
*Connectome
*Drosophila melanogaster/physiology
*Neuropeptides/metabolism/genetics
Neurons/physiology/metabolism
Temperature
Wakefulness/physiology
Feedback, Physiological
Brain/physiology/metabolism
Drosophila/physiology
Cues
Signal Transduction

@article {pmid39619679,
year = {2024},
author = {Zhang, W and Tang, X and Wang, M},
title = {Attention model of EEG signals based on reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1442398},
pmid = {39619679},
issn = {1662-5161},
abstract = {BACKGROUND: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.

METHODS: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.

RESULTS: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.

CONCLUSION: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.},
}

@article {pmid39619531,
year = {2024},
author = {Patel, M and Gosai, J and Patel, P and Roy, M and Solanki, A},
title = {Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.},
journal = {ACS omega},
volume = {9},
number = {47},
pages = {46841-46850},
pmid = {39619531},
issn = {2470-1343},
abstract = {Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼10[3] s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.},
}

@article {pmid39615858,
year = {2024},
author = {Xue, Z and Zhong, W and Cao, Y and Liu, S and An, X},
title = {Impact of different auditory environments on task performance and EEG activity.},
journal = {Brain research bulletin},
volume = {220},
number = {},
pages = {111142},
doi = {10.1016/j.brainresbull.2024.111142},
pmid = {39615858},
issn = {1873-2747},
abstract = {Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.},
}

@article {pmid39615554,
year = {2024},
author = {Cai, Z and Gao, Y and Fang, F and Zhang, Y and Du, S},
title = {Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110332},
doi = {10.1016/j.jneumeth.2024.110332},
pmid = {39615554},
issn = {1872-678X},
abstract = {In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.},
}

@article {pmid39612132,
year = {2024},
author = {Yang, Y and Li, M and Wang, L},
title = {An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {39612132},
issn = {1741-0444},
abstract = {Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.},
}

@article {pmid39609786,
year = {2024},
author = {Sun, J and Chen, S and Wang, S and Guo, H and Wang, X},
title = {The relationship between work-family conflict, stress and depression among Chinese correctional officers: a mediation and network analysis study.},
journal = {BMC public health},
volume = {24},
number = {1},
pages = {3317},
pmid = {39609786},
issn = {1471-2458},
support = {2021ZD0200700//the 2030 Plan Technology and Innovation of China/ ; },
mesh = {Humans ; *Depression/epidemiology/psychology ; Male ; Adult ; Cross-Sectional Studies ; China/epidemiology ; Female ; Middle Aged ; Mediation Analysis ; Stress, Psychological/epidemiology/psychology ; Prisons ; Family/psychology ; Occupational Stress/psychology/epidemiology ; Police/psychology/statistics & numerical data ; Social Network Analysis ; Correctional Facilities Personnel ; East Asian People ; },
abstract = {BACKGROUND: Numerous studies have found that depression is prevalent among correctional officers (COs), which may be related to the work-family conflict (WFC) faced by this cohort. Role conflict theory posits that WFC emerges from the incompatibility between the demands of work and family roles, which induces stress and, in turn, results in emotional problems. Thus, this study seeks to investigate the association between WFC and depression, along with examining the mediating role of stress. Further network analysis is applied to identify the core and bridge symptoms within the network of WFC, stress, and depression, providing a basis for targeted interventions.

OBJECTIVE: This study aims to investigate the relationship between work-family conflict (WFC) and depressive symptoms among a larger sample of Chinese correctional officers (COs), exploring the potential mechanisms of stress in this population through network analysis.

METHODS: A cross-sectional study of 472 Chinese COs was conducted from October 2021 to January 2022. WFC, stress, and depressive symptoms were evaluated using the Work-Family Conflict Scale (WFCS) and the Depression Anxiety Stress Scale (DASS). Subsequently, correlation and regression analyses were conducted using SPSS 26.0, while mediation analysis was performed using Model 4 in PROCESS. By using the EBICglasso model, network analyses were utilized to estimate the network structure of WFC, stress and depression. Visualization and centrality measures were performed using the R package.

RESULTS: The results showed that (1) there was a significant positive correlation between WFC and stress and depression, as well as between stress and depression, (2) WFC and stress had a significant positive predictive effect on depression, (3) stress mediated the relationship between WFC and depression, with a total mediating effect of 0.262 (BootSE = 0.031, BCI 95% = 0.278, 0.325), which accounted for 81.62% of the total effect, and (4) in the WFC, stress, and depression network model, strain-based work interference with family (SWF, (betweenness = 2.24, closeness = -0.19, strength = 1.40), difficult to relax (DR, betweenness = 1.20, closeness = 1.85, strength = 1.06), and had nothing (HN, betweenness = -0.43, closeness = 0.62, strength = 0.73) were the core symptoms, and SWF, IT, and DH were the bridge symptoms, and (5) first-line COs had significantly higher levels of WFC, stress, and depression than non-first-line correctional officers.

CONCLUSION: Our findings elucidate the interrelationships between WFC, stress, and depression among COs. The study also enhances the understanding of the factors influencing WFC in this population and provides valuable guidance for the development of future interventions, offering practical clinical significance.},
}

Humans
*Depression/epidemiology/psychology
Male
Adult
Cross-Sectional Studies
China/epidemiology
Female
Middle Aged
Mediation Analysis
Stress, Psychological/epidemiology/psychology
Prisons
Family/psychology
Occupational Stress/psychology/epidemiology
Police/psychology/statistics & numerical data
Social Network Analysis
Correctional Facilities Personnel
East Asian People

@article {pmid39609406,
year = {2024},
author = {Huang, W and Jin, N and Guo, J and Shen, C and Xu, C and Xi, K and Bonhomme, L and Quast, RB and Shen, DD and Qin, J and Liu, YR and Song, Y and Gao, Y and Margeat, E and Rondard, P and Pin, JP and Zhang, Y and Liu, J},
title = {Structural basis of orientated asymmetry in a mGlu heterodimer.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10345},
pmid = {39609406},
issn = {2041-1723},
support = {ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-10-INBS-04//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure ; *Cryoelectron Microscopy ; Humans ; *Protein Multimerization ; HEK293 Cells ; Allosteric Regulation ; Glutamic Acid/metabolism/chemistry ; Models, Molecular ; Fluorescence Resonance Energy Transfer ; Animals ; Protein Binding ; Binding Sites ; },
abstract = {The structural basis for the allosteric interactions within G protein-coupled receptors (GPCRs) heterodimers remains largely unknown. The metabotropic glutamate (mGlu) receptors are complex dimeric GPCRs important for the fine tuning of many synapses. Heterodimeric mGlu receptors with specific allosteric properties have been identified in the brain. Here we report four cryo-electron microscopy structures of mGlu2-4 heterodimer in different states: an inactive state bound to antagonists, two intermediate states bound to either mGlu2 or mGlu4 agonist only and an active state bound to both glutamate and a mGlu4 positive allosteric modulator (PAM) in complex with Gi protein. In addition to revealing a unique PAM binding pocket among mGlu receptors, our data bring important information for the asymmetric activation of mGlu heterodimers. First, we show that agonist binding to a single subunit in the extracellular domain is not sufficient to stabilize an active dimer conformation. Single-molecule FRET data show that the monoliganded mGlu2-4 can be found in both intermediate states and an active one. Second, we provide a detailed view of the asymmetric interface in seven-transmembrane (7TM) domains and identified key residues within the mGlu2 7TM that limits its activation leaving mGlu4 as the only subunit activating G proteins.},
}

*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure
*Cryoelectron Microscopy
Humans
*Protein Multimerization
HEK293 Cells
Allosteric Regulation
Glutamic Acid/metabolism/chemistry
Models, Molecular
Fluorescence Resonance Energy Transfer
Animals
Protein Binding
Binding Sites

@article {pmid39606007,
year = {2024},
author = {Chang, C and Piao, Y and Zhang, M and Liu, Y and Du, M and Yang, M and Mei, T and Wu, C and Wang, Y and Chen, X and Zeng, GQ and Zhang, X},
title = {Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1441533},
pmid = {39606007},
issn = {1664-0640},
abstract = {BACKGROUND: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.

OBJECTIVE: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.

METHODS: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.

RESULTS: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.

CONCLUSION: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.},
}

@article {pmid39605556,
year = {2024},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.12.623096},
pmid = {39605556},
issn = {2692-8205},
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.},
}

@article {pmid39605372,
year = {2024},
author = {Kumaresan, V and Hung, CY and Hermann, BP and Seshu, J},
title = {Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.20.624562},
pmid = {39605372},
issn = {2692-8205},
abstract = {UNLABELLED: Borrelia burgdorferi (Bb) , the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (Bb LP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. S ingle- c ell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, du al s pecificity p hosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to Bb LP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to Bb LP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5 , Il1b , and Cd274 . Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts.

IMPORTANCE: Borrelia burgdorferi , the agent of Lyme disease, encodes numerous lipoproteins that play a crucial role as a pathogen associated molecular pattern affecting interactions with tick- and vertebrate-host cells. Single cell transcriptomics validated using unbiased proteomics and conventional molecular biology approaches have demonstrated significant differences in gene expression patterns in a dose- and time-dependent manner following treatment of murine bone marrow derived macrophages with borrelial lipoproteins. Distinct populations of macrophages, alterations in immune signaling pathways, cellular energy production and mitochondrial responses were identified and validated using primary murine macrophages and human reporter cell lines. Notably, the role of Dual Specificity Phosphatase 1 (DUSP1) in influencing several inflammatory, metabolic and mitochondrial responses of macrophages were observed in these studies using known pharmacological inhibitors. Significant outcomes include novel strategies to interfere with immunomodulatory and survival capabilities of B. burgdorferi in reservoir hosts affecting its natural infectious life cycle between ticks and vertebrate hosts.},
}

@article {pmid39603445,
year = {2024},
author = {Kerezoudis, P and Jensen, MA and Huang, H and Ojemann, JG and Klassen, BT and Ince, NF and Hermes, D and Miller, KJ},
title = {Spatial and spectral changes in cortical surface Potentials during pinching versusThumb and index finger flexion.},
journal = {Neuroscience letters},
volume = {},
number = {},
pages = {138062},
doi = {10.1016/j.neulet.2024.138062},
pmid = {39603445},
issn = {1872-7972},
abstract = {Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.},
}

@article {pmid39600948,
year = {2024},
author = {Adams, M and Cottrell, J},
title = {Development and characterization of an in vitro fluorescently tagged 3D bone-cartilage interface model.},
journal = {Frontiers in endocrinology},
volume = {15},
number = {},
pages = {1484912},
pmid = {39600948},
issn = {1664-2392},
mesh = {Animals ; Mice ; *Cell Differentiation ; *Osteoblasts/metabolism/cytology ; *Chondrocytes/metabolism/cytology ; *Osteoclasts/metabolism/cytology ; Bone and Bones/metabolism/cytology ; Cartilage/metabolism/cytology ; RAW 264.7 Cells ; Osteocytes/metabolism/cytology ; Cell Culture Techniques, Three Dimensional/methods ; Osteogenesis/physiology ; Cell Line ; },
abstract = {Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.},
}

Animals
Mice
*Cell Differentiation
*Osteoblasts/metabolism/cytology
*Chondrocytes/metabolism/cytology
*Osteoclasts/metabolism/cytology
Bone and Bones/metabolism/cytology
Cartilage/metabolism/cytology
RAW 264.7 Cells
Osteocytes/metabolism/cytology
Cell Culture Techniques, Three Dimensional/methods
Osteogenesis/physiology
Cell Line

@article {pmid39600168,
year = {2024},
author = {Amandusson, Å and Nilsson, J and Pequito, S},
title = {[The role of EEG in tomorrow's medicine].},
journal = {Lakartidningen},
volume = {121},
number = {},
pages = {},
pmid = {39600168},
issn = {1652-7518},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Wearable Electronic Devices ; Artificial Intelligence ; Brain/physiology ; },
abstract = {There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.},
}

Humans
*Electroencephalography
*Brain-Computer Interfaces
Wearable Electronic Devices
Artificial Intelligence
Brain/physiology

@article {pmid39598903,
year = {2024},
author = {Angulo Medina, AS and Aguilar Bonilla, MI and Rodríguez Giraldo, ID and Montenegro Palacios, JF and Cáceres Gutiérrez, DA and Liscano, Y},
title = {Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {22},
pages = {},
doi = {10.3390/s24227125},
pmid = {39598903},
issn = {1424-8220},
support = {Convocatoria Interna No. 01-2024//Universidad Santiago de Cali/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Bibliometrics ; Artificial Intelligence ; Rehabilitation/methods ; Brain/physiology ; },
abstract = {EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Bibliometrics
Artificial Intelligence
Rehabilitation/methods
Brain/physiology

@article {pmid39597167,
year = {2024},
author = {Li, W and Zhou, J and Sheng, W and Jia, Y and Xu, W and Zhang, T},
title = {Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/mi15111355},
pmid = {39597167},
issn = {2072-666X},
support = {12072151 and 12472153//National Natural Science Foundation of China/ ; 2019YFA0705400//National Key Research and Development Program of China/ ; MCAS-E-0124Y03//Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures/ ; },
abstract = {A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa[-1] within the low-pressure range of 0-390 Pa and 0.012 kPa[-1] within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.},
}

@article {pmid39597097,
year = {2024},
author = {Ji, W and Su, H and Jin, S and Tian, Y and Li, G and Yang, Y and Li, J and Zhou, Z and Wei, X and Tao, TH and Qin, L and Ye, Y and Sun, L},
title = {A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/mi15111283},
pmid = {39597097},
issn = {2072-666X},
support = {2022YFF0706500//National Key R & D Program of China/ ; 2022ZD0209300//National Key R & D Program of China/ ; 2021ZD0201600//National Key R & D Program of China/ ; 2019YFA0905200//National Key R & D Program of China/ ; 2021YFC2501500//National Key R & D Program of China/ ; 2021YFF1200700//National Key R & D Program of China/ ; 2022ZD0212300//National Key R & D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21PJ1415100//Shanghai Pujiang Program/ ; 19PJ1410900//Shanghai Pujiang Program/ ; 21JM0010200//Science and Technology Commission Foundation of Shanghai/ ; 21142200300//Science and Technology Commission Foundation of Shanghai/ ; 22QA1410900//Shanghai Rising-Star Program/ ; 22YF1454700//Shanghai Sailing Program/ ; 20212ABC03W07//Jiangxi Province 03 Special Project and 5G Project/ ; 20201ZDE04013//Fund for Central Government in Guidance of Local Science and Technology Development/ ; 2021B0909060002//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; 2021B0909050004//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; },
abstract = {Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.},
}

@article {pmid39595855,
year = {2024},
author = {Tubbs, A and Vazquez, EA},
title = {Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review.},
journal = {Brain sciences},
volume = {14},
number = {11},
pages = {},
doi = {10.3390/brainsci14111092},
pmid = {39595855},
issn = {2076-3425},
abstract = {In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact.},
}

@article {pmid39593731,
year = {2024},
author = {Nuñez Ponasso, G and Wartman, WA and McSweeney, RC and Lai, P and Haueisen, J and Maess, B and Knösche, TR and Weise, K and Noetscher, GM and Raij, T and Makaroff, SN},
title = {Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {11},
pages = {},
pmid = {39593731},
issn = {2306-5354},
support = {R01EB035484/EB/NIBIB NIH HHS/United States ; R01MH130490/MH/NIMH NIH HHS/United States ; 1R01NS126337/NS/NINDS NIH HHS/United States ; 01GQ2201//Bundesministerium für Bildung und Forschung/ ; 01GQ2304A//Bundesministerium für Bildung und Forschung/ ; 2018 IZN 004//Free State of Thuringia/ ; 2018 IZN 004//European Regional Development Fund/ ; },
abstract = {Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10-20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.},
}

@article {pmid39592732,
year = {2024},
author = {Zheng, MM and Li, JY and Guo, HJ and Zhang, J and Wang, LS and Jiang, KF and Wu, HH and He, QJ and Ding, L and Yang, B},
title = {IMPDH inhibitors upregulate PD-L1 in cancer cells without impairing immune checkpoint inhibitor efficacy.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {39592732},
issn = {1745-7254},
abstract = {Tumor cells are characterized by rapid proliferation. In order to provide purines for DNA and RNA synthesis, inosine 5'-monophosphate dehydrogenase (IMPDH), a key enzyme in the de novo guanosine biosynthesis, is highly expressed in tumor cells. In this study we investigated whether IMPDH was involved in cancer immunoregulation. We revealed that the IMPDH inhibitors AVN944, MPA or ribavirin concentration-dependently upregulated PD-L1 expression in non-small cell lung cancer cell line NCI-H292. This effect was reproduced in other non-small cell lung cancer cell lines H460, H1299 and HCC827, colon cancer cell lines HT29, RKO and HCT116, as well as kidney cancer cell line Huh7. In NCI-H292 cells, we clarified that IMPDH inhibitors increased CD274 mRNA levels by enhancing CD274 mRNA stability. IMPDH inhibitors improved the affinity of the ARE-binding protein HuR for CD274 mRNA, thereby stabilizing CD274 mRNA. Guanosine supplementation abolished the IMPDH inhibitor-induced increase in PD-L1 expression. In CT26 and EMT6 tumor models used for ICIs based studies, we showed that despite its immunosuppressive properties, the IMPDH inhibitor mycophenolate mofetil did not reduce the clinical response of checkpoint inhibitors, representing an important clinical observation given that this class of drugs is approved for use in multiple diseases. We conclude that PD-L1 induction contributes to the immunosuppressive effect of IMPDH inhibitors. Furthermore, the IMPDH inhibitor mycophenolate mofetil does not antagonize immune checkpoint blockade.},
}

@article {pmid39592434,
year = {2024},
author = {Paban, V and Feraud, L and Weills, A and Duplan, F},
title = {Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.},
journal = {The European journal of neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1111/ejn.16621},
pmid = {39592434},
issn = {1460-9568},
support = {214535, UMR7077//Janssen Horizon/ ; },
abstract = {IMPACT STATEMENT: This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals.

ABSTRACT: In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.},
}

@article {pmid39591745,
year = {2024},
author = {Smedemark-Margulies, N and Wang, Y and Koike-Akino, T and Liu, J and Parsons, K and Bicer, Y and Erdogmus, D},
title = {Improving subject transfer in EEG classification with divergence estimation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9777},
pmid = {39591745},
issn = {1741-2552},
abstract = {\textit{Objective}.
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training. \textit{Approach}.
We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models. \textit{Main Results}.
We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We first show the performance of each method across a wide range of hyperparameters, demonstrating that each method can be easily tuned to yield significant benefits over an unregularized model. We show that, using ideal hyperparameters for all methods, our first technique gives significantly better performance than the baseline regularization technique. We also show that, across hyperparameters, our second technique gives significantly more stable performance than the baseline. The proposed methods require only a small computational cost at training time that is equivalent to the cost of the baseline. \textit{Significance}.
The high variability in signal distribution between subjects means that typical approaches to EEG signal modeling often require time-intensive calibration for each user, and even re-calibration before every use. By improving the performance of population models in the most stringent case of zero-shot subject transfer, we may help reduce or eliminate the need for model calibration.},
}

@article {pmid39591752,
year = {2024},
author = {Sun, Y and Zhang, F and Li, Z and Liu, X and Zheng, D and Zhang, S and Fan, S and Wu, X},
title = {Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9778},
pmid = {39591752},
issn = {1741-2552},
abstract = {Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility in comparison to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification in ear-EEG faces a major challenge due to the significant attenuation and distorted amplitude of the signal. Our aim is to enhance the classification performance of SSVEP using ear-EEG and augment its practical application value. To address this challenge, we focuse on enhancing ear-EEG feature representations by training the model to learn feature representations similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed for optimizing SSVEP target classification recognition in ear-EEG data. This framework combines signals from the scalp area to obtains multi-layer distilled knowledge through the cooperation of distillation of features in the mid-layer feature distillation and output layer response distillation. We improved the classification of the shorter first 1s data and achieved a maximum classification result of 75.7%. We evaluate the proposed MESD framework through single-session, cross-session and cross-subject transfer decoding, comparing it with baseline method. The results demonstrate that the proposed framework achieves the best classification results in all experiments. Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks including auxiliary control and rehabilitation training in forthcoming endeavors.},
}

@article {pmid39590012,
year = {2024},
author = {AlQahtani, NJ and Al-Naib, I and Ateeq, IS and Althobaiti, M},
title = {Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.},
journal = {Biosensors},
volume = {14},
number = {11},
pages = {},
doi = {10.3390/bios14110553},
pmid = {39590012},
issn = {2079-6374},
support = {KSRG-2023-195//King Salman Center for Disability Research/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared ; *Electromyography ; Male ; Adult ; Knee Prosthesis ; Knee/physiology ; Brain-Computer Interfaces ; Artificial Limbs ; },
abstract = {The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain-computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.},
}

Humans
*Spectroscopy, Near-Infrared
*Electromyography
Male
Adult
Knee Prosthesis
Knee/physiology
Brain-Computer Interfaces
Artificial Limbs

@article {pmid39589888,
year = {2024},
author = {Liu, J and Li, Z and Sun, M and Zhou, L and Wu, X and Lu, Y and Shao, Y and Liu, C and Huang, N and Hu, B and Wu, Z and You, C and Li, L and Wang, M and Tao, L and Di, Z and Sheng, X and Mei, Y and Song, E},
title = {Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {49},
pages = {e2412423121},
doi = {10.1073/pnas.2412423121},
pmid = {39589888},
issn = {1091-6490},
support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057 62304044 12022209//the National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; LG-QS-202202-02//Lingang Laboratory/ ; },
mesh = {Animals ; Humans ; *Inflammation/therapy ; *Wound Healing ; Hydrogels/chemistry ; Wearable Electronic Devices ; Temperature ; Monitoring, Physiologic/instrumentation/methods ; Rats ; Biosensing Techniques/instrumentation/methods ; },
abstract = {Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.},
}

Animals
Humans
*Inflammation/therapy
*Wound Healing
Hydrogels/chemistry
Wearable Electronic Devices
Temperature
Monitoring, Physiologic/instrumentation/methods
Rats
Biosensing Techniques/instrumentation/methods

@article {pmid39589717,
year = {2024},
author = {Chan, RW and Edelman, BJ and Tsang, SY and Gao, K and Yu, AC},
title = {Opportunities for System Neuroscience.},
journal = {Advances in neurobiology},
volume = {41},
number = {},
pages = {247-253},
pmid = {39589717},
issn = {2190-5215},
mesh = {Humans ; *Neurosciences ; *Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuroimaging ; Precision Medicine ; Nerve Net/diagnostic imaging/physiology ; },
abstract = {Systems neuroscience explores the intricate organization and dynamic function of neural circuits and networks within the brain. By elucidating how these complex networks integrate to execute mental operations, this field aims to deepen our understanding of the biological basis of cognition, behavior, and consciousness. In this chapter, we outline the promising future of systems neuroscience, highlighting the emerging opportunities afforded by powerful technological innovations and their applications. Cutting-edge tools such as awake functional MRI, ultrahigh field strength neuroimaging, functional ultrasound imaging, and optoacoustic techniques have revolutionized the field, enabling unprecedented observation and analysis of brain activity. The insights gleaned from these advanced methodologies have empowered the development of a suite of exciting applications across diverse domains. These include brain-machine interfaces (BMIs) for neural prosthetics, cognitive enhancement therapies, personalized mental health interventions, and precision medicine approaches. As our comprehension of neural systems continues to grow, it is envisioned that these and related applications will become increasingly refined and impactful in improving human health and well-being.},
}

Humans
*Neurosciences
*Brain/diagnostic imaging/physiology
*Brain-Computer Interfaces
Magnetic Resonance Imaging
Neuroimaging
Precision Medicine
Nerve Net/diagnostic imaging/physiology

@article {pmid39588722,
year = {2024},
author = {Xiang, Z and Yang, L and Yu, B and Zeng, Q and Huang, T and Shi, S and Yu, H and Zhang, Y and Wu, J and Zhu, M},
title = {Recent advances in polymer-based thin-film electrodes for ECoG applications.},
journal = {Journal of materials chemistry. B},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4tb02090a},
pmid = {39588722},
issn = {2050-7518},
abstract = {Electrocorticography (ECoG) has garnered widespread attention owing to its superior signal resolution compared to conventional electroencephalogram (EEG). While ECoG signal acquisition entails invasiveness, the invasive rigid electrode used inevitably inflicts damage on brain tissue. Polymer electrodes that combine conductivity and transparency have garnered great interest because they not only facilitate high-quality signal acquisition but also provide additional insights while preserving the health of the brain, positioning them as the future frontier in the brain-computer interface (BCI). This review summarizes the multifaceted functions of polymers in ECoG thin-film electrodes for the BCI. We present the abilities of sensitive and structural polymers focusing on impedance reduction, signal quality improvement, good flexibility, and transparency. Typically, two sensitive polymers and four structural polymers are analyzed in detail in terms of ECoG electrode properties. Moreover, the underlying mechanism of polymer-based electrodes in signal quality enhancement is revealed. Finally, the remaining challenges and perspectives are discussed.},
}

@article {pmid39588687,
year = {2024},
author = {Wang, J and Jiang, Y and Xiong, T and Lu, J and He, X and Yu, P and Mao, L},
title = {Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e202418949},
doi = {10.1002/anie.202418949},
pmid = {39588687},
issn = {1521-3773},
abstract = {Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.},
}

@article {pmid39586499,
year = {2024},
author = {Thenmozhi, T and Helen, R and Mythili, S},
title = {Classification of Motor Imagery EEG with Ensemble RNCA model.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115345},
doi = {10.1016/j.bbr.2024.115345},
pmid = {39586499},
issn = {1872-7549},
abstract = {Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22% and 91.62% for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75%. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.},
}

@article {pmid39586421,
year = {2024},
author = {Hong, T and Zhou, H and Xi, W and Li, X and Du, Y and Liu, J and Geng, F and Hu, Y},
title = {Acting with awareness is positively correlated with dorsal anterior cingulate cortex glutamate concentration but both are impaired in Internet gaming disorder.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.054},
pmid = {39586421},
issn = {1873-7544},
abstract = {Internet gaming disorder (IGD) is increasingly recognized as a public concern for its adverse impacts on cognition and mental health. In IGD, the transition from goal-directed actions to habitual and eventually compulsive behaviors is accompanied by altered neural response within the dorsal anterior cingulate cortex (dACC), a critical region involved in conscious actions. However, the neurochemical profile of the dACC in IGD and its relationship with behavioral awareness remain poorly understood. In this study, [1]H-magnetic resonance spectroscopy was employed to quantify dACC glutamate concentration and examine its association with the capacity for 'acting with awareness' among 21 participants with IGD and 19 recreational game users. Results indicated that dACC glutamate levels and behavioral awareness were significantly lower in the IGD group compared to recreational game users. Moreover, a significant positive correlation between awareness and dACC glutamate concentration emerged in the recreational game users' group, a relationship attenuated in those with IGD. In an independent cohort of 107 participants, the positive association between awareness and dACC glutamate concentration was replicated. These findings suggest that reduced dACC glutamate in IGD may underlie diminished awareness of maladaptive habitual behaviors. Enhancing dACC neural excitability through neuromodulation or mindfulness training could represent a potential intervention to restore behavioral awareness.},
}

@article {pmid39586422,
year = {2024},
author = {Mao, W and Shen, X and Bai, X and Wang, A},
title = {Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.044},
pmid = {39586422},
issn = {1873-7544},
abstract = {Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait empathy influence prosocial behavior. Addressing this knowledge gap is important to understand empathy-driven prosocial behavior. We employed an EEG experiment combined with interpretable machine learning methods to probe these questions. We found that empathic concern (EC) played the most pivotal role in donation decision. Behaviorally, EC negatively moderates the effect of perceived closeness and deservedness of charity projects on the willingness to donate. The machine learning results indicate that EC significantly predicts late positive potential (LPP) and beta-band activity during donation information processing. Further regression analysis results indicate that EC, rather than other dimensions of trait empathy, can positively predict LPP amplitude and negatively predict beta-band activity. These results indicated that participants with higher EC scores may experience heightened emotional arousal and the vicarious experience of others' emotions while processing donation information. Our work adds weight to understanding the relationship between trait empathy and prosocial behavior and provides electrophysiological evidence.},
}

@article {pmid39586380,
year = {2024},
author = {Higashi, H},
title = {Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110323},
doi = {10.1016/j.jneumeth.2024.110323},
pmid = {39586380},
issn = {1872-678X},
abstract = {Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain-computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.},
}

@article {pmid39583262,
year = {2024},
author = {Abo Alzahab, N and Iorio, AD and Apollonio, L and Alshalak, M and Gravina, A and Antognoli, L and Baldi, M and Scalise, L and Alchalabi, B},
title = {Auditory evoked potential electroencephalography-biometric dataset.},
journal = {Data in brief},
volume = {57},
number = {},
pages = {111065},
pmid = {39583262},
issn = {2352-3409},
abstract = {This work aims to assess the use of electroencephalographic (EEG) signals as a means of biometric authentication. More than 240 recordings, each lasting 2 min, were gathered from 20 subjects involved in the data collection. Data include the results of experiments performed both in a resting state and in the presence of auditory stimuli. The resting-state EEG signals were acquired with both open and closed eyes. The auditory stimuli EEG signals consist of six experiments divided into two scenarios. The first scenario considers in-ear stimuli, while the second scenario considers bone-conducting stimuli. For each of the two scenarios, experiments include a native language song, a non-native language song and some neutral music. This data could be used to develop biometric systems for authentication or identification. Additionally, they could be used to study the effect of auditory stimuli such as music on EEG activity and to compare it with the resting state condition.},
}

@article {pmid39582417,
year = {2024},
author = {Wang, X and Zhu, K and Wu, W and Zhou, D and Lu, H and Du, J and Cai, L and Yan, X and Li, W and Qian, X and Wang, X and Ma, C and Hu, Y and Tian, C and Sun, B and Fang, Z and Wu, J and Jiang, P and Liu, J and Liu, C and Fan, J and Cui, H and Shen, Y and Duan, S and Bao, A and Yang, Y and Qiu, W and Zhang, J},
title = {Prevalence of mixed neuropathologies in age-related neurodegenerative diseases: A community-based autopsy study in China.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {},
number = {},
pages = {},
doi = {10.1002/alz.14369},
pmid = {39582417},
issn = {1552-5279},
support = {2021ZD0201100//STI2030-Major Project/ ; 2024C03098//Key R&D Program of Zhejiang province/ ; 2024SSYS0018//Key R&D Program of Zhejiang province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; 2021-I2M-1-025//CAMS Innovation Fund for Medical Sciences/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 81971184//National Natural Science Foundation of China/ ; },
abstract = {INTRODUCTION: Despite extensive studies on mixed neuropathologies, data from China are limited. This study aims to fill this gap by analyzing brain samples from Chinese brain banks.

METHODS: A total of 1142 brains from six Chinese brain banks were examined using standardized methods. Independent pathologists conducted evaluations with stringent quality control. Prevalence and correlations of neurological disorders were analyzed.

RESULTS: Significant proportions of brains displayed primary age-related tauopathy (PART, 35%), limbic-predominant age-related TDP-43 encephalopathy (LATE, 46%), and aging-related tau astrogliopathy (ARTAG, 12%). Alzheimer's disease neuropathological change (ADNC, 48%), Lewy body disease (LBD, 13%), and cerebrovascular disease (CVD, 63%) were also prevalent, often co-occurring with regional variations. CVD emerged as the potential most early contributor to neuropathological changes.

DISCUSSION: This analysis highlights the prevalence of PART, LATE, ARTAG, ADNC, LBD, and CVD, with regional differences. The findings suggest CVD may be the earliest contributing factor, potentially preceding other neuropathologies. Highlights The prevalence of primary age-related tauopathy (PART), limbic-predominant age-related TDP-43 encephalopathy (LATE), aging-related tau astrogliopathy (ARTAG), Alzheimer's disease neuropathologic change, Lewy body disease, and cerebrovascular disease (CVD) in China, increasing with age, is comparable to other countries. Significant regional differences in the prevalences of diseases are noted. CVD develops prior to any other disorders, including PART, LATE, and ARTAG.},
}

@article {pmid39581346,
year = {2024},
author = {Brands, R and Bartsch, J and Thommes, M},
title = {Complemental Hard Modeling in Raman spectroscopy - A case study on titanium dioxide-free coating in-line monitoring.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.xphs.2024.10.044},
pmid = {39581346},
issn = {1520-6017},
abstract = {Tablets are coated for taste or odor modification, for modified release profiles or as a protective layer to increase the stability. Here, titanium dioxide is frequently added as a coating component due to its opaque properties. Furthermore, its Raman activity makes it an integral part of in-line monitoring models. However, due to the carcinogenic potential of titanium dioxide, calcium carbonate is utilized as a substitute, exhibiting similar opaque properties. However, calcium carbonate tends to exhibit overlapped peaks with carbon hydrates in the Raman spectrum. Consequently, new models based on e.g. hard modeling are required instead of peak integration. In this study, tablets were coated with a coating including calcium carbonate. Partial Least Squares Regression (PLS) and Complemental Hard Modeling (CHM) were examined as feasible in-line monitoring approaches. Furthermore, two different measurement positions in the coater were compared, orthogonal and tangential with respect to the moving tablet bed. Cross-validation exhibited improved CHM performance with reduced RMSECV values of about 5%. The prediction of the coating mass growth occurred comparable with RMSEP values in a similar range of 2-5%. Despite this, the CHM´s achieved improved performance with reduced training data quantity and quality. The different measurement positions indicated no process-relevant differences.},
}

@article {pmid39581146,
year = {2024},
author = {Anwar, F and Zhang, K and Sun, C and Pang, M and Zhou, W and Li, H and He, R and Liu, X and Ming, D},
title = {Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research.},
journal = {Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie},
volume = {181},
number = {},
pages = {117702},
doi = {10.1016/j.biopha.2024.117702},
pmid = {39581146},
issn = {1950-6007},
abstract = {Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus.},
}

@article {pmid39577701,
year = {2024},
author = {Xia, G and Wang, L and Xiong, S and Deng, J},
title = {Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis.},
journal = {Journal of neuroscience methods},
volume = {414},
number = {},
pages = {110325},
doi = {10.1016/j.jneumeth.2024.110325},
pmid = {39577701},
issn = {1872-678X},
abstract = {BACKGROUND: In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.

NEW METHOD: With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.

RESULTS: The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7 s, the average recognition accuracy of the subjects increases by 10.71 % on the Benchmark dataset and by 6.98 % on the BETA dataset, respectively.

Our extended method outperforms the state-of-the-art methods by at least 3 %, and it effectively suppresses local noise and maintains excellent scalability.

The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.},
}

@article {pmid39577098,
year = {2024},
author = {Oxley, T},
title = {A 10-year journey towards clinical translation of an implantable endovascular BCI A keynote lecture given at the BCI society meeting in Brussels.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9633},
pmid = {39577098},
issn = {1741-2552},
abstract = {In the rapidly evolving field of brain-computer interfaces (BCIs), a novel modality for recording electrical brain signals has quietly emerged over the past decade. The technology is endovascular electrocorticography, an innovation that stands alongside well-established methods such as electroencephalography (EEG), traditional electrocorticography (ECoG), and single/multi-unit activity recording. This system was inspired by advancements in interventional cardiology, particularly the integration of electronics into various medical interventions. This breakthrough led to the development of the Stentrode system, which employs stent-mounted electrodes to record electrical brain activity for applications in a motor neuroprosthesis. This Perspective explores four key areas in our quest to bring the Stentrode BCI to market: the critical patient need for autonomy driving our efforts, the hurdles and achievements in assessing BCI performance, the compelling advantages of our unique endovascular approach, and the essential steps for clinical translation and product commercialization.},
}

@article {pmid39576281,
year = {2024},
author = {Farina, D and Merletti, R and Enoka, RM},
title = {The Extraction Of Neural Strategies From The Surface Emg: 2004-2024.},
journal = {Journal of applied physiology (Bethesda, Md. : 1985)},
volume = {},
number = {},
pages = {},
doi = {10.1152/japplphysiol.00453.2024},
pmid = {39576281},
issn = {1522-1601},
support = {810346//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; RG-2206-39688//National Multiple Sclerosis Society/ ; EP/T020970/1//UKRI | Engineering and Physical Sciences Research Council (EPSRC)/ ; },
abstract = {This review follows two previous papers (Farina et al., 2004, 2014) in which we reflected on the use of surface EMG in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modelling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of non-measurable parameters by inverse modelling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the non-stationarities in dynamic contractions, and to decompose signals in real-time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 years since our first review, we conclude that the recording and analysis of surface EMG signals has seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.},
}

@article {pmid39572612,
year = {2024},
author = {Lee, AH and Lee, J and Leung, V and Larson, L and Nurmikko, A},
title = {Patterned electrical brain stimulation by a wireless network of implantable microdevices.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10093},
pmid = {39572612},
issn = {2041-1723},
support = {232600//National Science Foundation (NSF)/ ; },
mesh = {Animals ; *Wireless Technology/instrumentation ; *Electric Stimulation/instrumentation/methods ; Rats ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Brain/physiology ; Male ; Motor Cortex/physiology ; Behavior, Animal/physiology ; },
abstract = {Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. Here, we introduce a wireless approach to multipoint patterned electrical microstimulation by a spatially distributed epicortically implanted network of silicon microchips to target specific areas of the cortex. Each sub-millimeter-sized microchip harvests energy from an external radio-frequency source and converts this into biphasic current injected focally into tissue by a pair of integrated microwires. The amplitude, period, and repetition rate of injected current from each chip are controlled across the implant network by implementing a pre-scheduled, collision-free bitmap wireless communication protocol featuring sub-millisecond latency. As a proof-of-concept technology demonstration, a network of 30 wireless stimulators was chronically implanted into motor and sensory areas of the cortex in a freely moving rat for three months. We explored the effects of patterned intracortical electrical stimulation on trained animal behavior at average RF powers well below regulatory safety limits.},
}

Animals
*Wireless Technology/instrumentation
*Electric Stimulation/instrumentation/methods
Rats
*Brain-Computer Interfaces
Electrodes, Implanted
Brain/physiology
Male
Motor Cortex/physiology
Behavior, Animal/physiology

@article {pmid39011811,
year = {2024},
author = {Blau, R and Russman, SM and Qie, Y and Shipley, W and Lim, A and Chen, AX and Nyayachavadi, A and Ah, L and Abdal, A and Esparza, GL and Edmunds, SJ and Vatsyayan, R and Dunfield, SP and Halder, M and Jokerst, JV and Fenning, DP and Tao, AR and Dayeh, SA and Lipomi, DJ},
title = {Surface-Grafted Biocompatible Polymer Conductors for Stable and Compliant Electrodes for Brain Interfaces.},
journal = {Advanced healthcare materials},
volume = {13},
number = {29},
pages = {e2402215},
doi = {10.1002/adhm.202402215},
pmid = {39011811},
issn = {2192-2659},
support = {FA9550-19-1-0278//Air Force Office of Scientific Research/ ; //National Institutes of Health's Brain Research/ ; UG3NS123723-01//BRAIN Initiative/ ; R01NS123655-01//BRAIN Initiative/ ; DP2-EB029757/EB/NIBIB NIH HHS/United States ; //UC President's Dissertation Year Fellowship/ ; //Natural Sciences and Engineering Research Council of Canada/ ; //Kuwait Foundation for the Advancement of Sciences/ ; CHE-1338173//National Science Foundation Major Research Instrumentation Program/ ; //UC Irvine Materials Research Institute/ ; //Alfred P. Sloan Foundation/ ; 898571//H2020 Marie Skłodowska-Curie Actions/ ; CBET-2223566//National Science Foundation Disability and Rehabilitation Engineering/ ; 1845683//National Science Foundation/ ; ECCS-1542148//National Science Foundation/ ; DMR-2011967//National Science Foundation/ ; DMR-2011924//National Science Foundation/ ; DP2-EB029757/EB/NIBIB NIH HHS/United States ; },
mesh = {*Polymers/chemistry ; *Biocompatible Materials/chemistry ; Surface Properties ; Electrodes ; Electric Conductivity ; Brain/physiology ; Brain-Computer Interfaces ; Animals ; Polyethylene Glycols/chemistry ; Gold/chemistry ; },
abstract = {Durable and conductive interfaces that enable chronic and high-resolution recording of neural activity are essential for understanding and treating neurodegenerative disorders. These chronic implants require long-term stability and small contact areas. Consequently, they are often coated with a blend of conductive polymers and are crosslinked to enhance durability despite the potentially deleterious effect of crosslinking on the mechanical and electrical properties. Here the grafting of the poly(3,4 ethylenedioxythiophene) scaffold, poly(styrenesulfonate)-b-poly(poly(ethylene glycol) methyl ether methacrylate block copolymer brush to gold, in a controlled and tunable manner, by surface-initiated atom-transfer radical polymerization (SI-ATRP) is described. This "block-brush" provides high volumetric capacitance (120 F cm[─3]), strong adhesion to the metal (4 h ultrasonication), improved surface hydrophilicity, and stability against 10 000 charge-discharge voltage sweeps on a multiarray neural electrode. In addition, the block-brush film showed 33% improved stability against current pulsing. This approach can open numerous avenues for exploring specialized polymer brushes for bioelectronics research and application.},
}

*Polymers/chemistry
*Biocompatible Materials/chemistry
Surface Properties
Electrodes
Electric Conductivity
Brain/physiology
Brain-Computer Interfaces
Animals
Polyethylene Glycols/chemistry
Gold/chemistry

@article {pmid39572577,
year = {2024},
author = {Zhang, Z and Ding, X and Bao, Y and Zhao, Y and Liang, X and Qin, B and Liu, T},
title = {Chisco: An EEG-based BCI dataset for decoding of imagined speech.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1265},
pmid = {39572577},
issn = {2052-4463},
support = {U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; *Imagination ; Adult ; Language ; Brain/physiology ; Young Adult ; },
abstract = {The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to "mind reading". However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject's EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
*Speech
*Imagination
Adult
Language
Brain/physiology
Young Adult

@article {pmid39571645,
year = {2024},
author = {Ma, J and Li, Z and Zheng, Q and Li, S and Zong, R and Qin, Z and Wan, L and Zhao, Z and Mao, Z and Zhang, Y and Yu, X and Bai, H and Zhang, J},
title = {Investigating Unilateral and Bilateral Motor Imagery Control Using Electrocorticography and fMRI in Awake Craniotomy.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {120949},
doi = {10.1016/j.neuroimage.2024.120949},
pmid = {39571645},
issn = {1095-9572},
abstract = {BACKGROUND: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.

OBJECTIVE: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs.

METHODS: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions.

RESULTS: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination.

CONCLUSION: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.},
}

@article {pmid39571386,
year = {2024},
author = {Li, Y and Chen, B and Yoshimura, N and Koike, Y and Yamashita, O},
title = {Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106899},
doi = {10.1016/j.neunet.2024.106899},
pmid = {39571386},
issn = {1879-2782},
abstract = {Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms. The goal of this paper is to propose a novel robust implementation of sparse Bayesian learning so that robustness and sparseness can be realized simultaneously. Motivated by the exceptional robustness of maximum correntropy criterion (MCC), we proposed integrating MCC to the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC, and then leveraged it for the likelihood function. Meanwhile, we utilized the automatic relevance determination technique as the sparse prior distribution. To fully evaluate the proposed method, a synthetic example and a real-world muscle activity reconstruction task with two different brain modalities were leveraged. Experimental results showed, our proposed sparse Bayesian correntropy learning framework significantly improves the robustness for the noisy regression tasks. Our proposed algorithm could realize higher correlation coefficients and lower root mean squared errors for the real-world muscle activity reconstruction scenario. Sparse Bayesian correntropy learning provides a powerful approach for brain activity decoding which will promote the development of brain-computer interface technology.},
}

@article {pmid39570849,
year = {2024},
author = {Liang, HJ and Li, LL and Cao, GZ},
title = {FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.},
journal = {PloS one},
volume = {19},
number = {11},
pages = {e0309706},
doi = {10.1371/journal.pone.0309706},
pmid = {39570849},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model.},
}

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

@article {pmid39570847,
year = {2024},
author = {Assiri, FY and Ragab, M},
title = {Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.},
journal = {PloS one},
volume = {19},
number = {11},
pages = {e0313261},
doi = {10.1371/journal.pone.0313261},
pmid = {39570847},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Humans ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Support Vector Machine ; Algorithms ; },
abstract = {Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people with a breakdown or motor damage, delivering a bridge between the brain's intentions and exterior actions. Employing electroencephalography (EEG) or aggressive neural recordings, machine learning (ML) methods are used to interpret patterns of brain action linked with motor image tasks. These models frequently depend upon models like support vector machine (SVM) or deep learning (DL) to distinguish among dissimilar MI classes, such as visualizing left or right limb actions. This procedure allows individuals, particularly those with motor disabilities, to utilize their opinions to command exterior devices like robotic limbs or computer borders. This article presents a Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning (BHHSHO-DL) technique based on Motor Imagery Classification for BCI. The BHHSHO-DL technique mainly exploits the hyperparameter-tuned DL approach for MI identification for BCI. Initially, the BHHSHO-DL technique performs data preprocessing utilizing the wavelet packet decomposition (WPD) model. Besides, the enhanced densely connected networks (DenseNet) model extracts the preprocessed data's complex and hierarchical feature patterns. Meanwhile, the BHHSHO technique-based hyperparameter tuning process is accomplished to elect optimal parameter values of the enhanced DenseNet model. Finally, the classification procedure is implemented by utilizing the convolutional autoencoder (CAE) model. The simulation value of the BHHSHO-DL methodology is performed on a benchmark dataset. The performance validation of the BHHSHO-DL methodology portrayed a superior accuracy value of 98.15% and 92.23% over other techniques under BCIC-III and BCIC-IV datasets.},
}

*Brain-Computer Interfaces
*Deep Learning
Humans
*Electroencephalography/methods
Imagination/physiology
Brain/physiology
Support Vector Machine
Algorithms

@article {pmid39569894,
year = {2024},
author = {Brannigan, JFM and Liyanage, K and Horsfall, HL and Bashford, L and Muirhead, W and Fry, A},
title = {Brain-computer interfaces patient preferences: a systematic review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a6},
pmid = {39569894},
issn = {1741-2552},
abstract = {Background Brain-computer interfaces (BCIs) have the potential to restore motor capabilities and functional independence in individuals with motor impairments. Despite accelerating advances in the performance of various implanted devices, few studies have identified patient preferences underlying device design, and moreover, each study has typically captured a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large patient cohort across multiple aetiologies. Methods We performed a systematic review of all published studies reporting patient preferences for BCI devices. We searched MEDLINE, Embase, and CINAHL from inception to April 18th, 2023. We included any study reporting either qualitative or quantitative preferences concerning BCI devices. Article screening and data extraction were performed by two reviewers in duplicate. Extracted information included demographic information, current digital device use, device invasiveness preference, device design preferences, and device functional preferences. Findings Our search identified 1316 articles, of which 28 studies were eligible for inclusion. Preference information was captured from 1701 patients (mean age = 42.1-64.3 years). Amyotrophic lateral sclerosis was the most represented clinical condition (n = 15 studies, 53.6%), followed by spinal cord injury (n = 13 studies, 46.4%). We found that individuals with motor impairment prioritise device accuracy over other device design characteristics. We also found that the speed and accuracy of BCI systems in recent publications exceeds reported patient preferences, however this performance has been achieved with a level of training and setup burden that would not be tolerated by most patients. When comparing populations across studies, we found that patient preferences vary according to both disease aetiology and the severity of motor impairment. Interpretation Our findings support a greater research emphasis on minimising BCI setup and training burden, and they suggest future BCI devices may require bespoke configuration and training for specific patient groups. .},
}

@article {pmid39569892,
year = {2024},
author = {Estiveira, J and Soares, E and Pires, G and Nunes, UJ and Sousa, T and Ribeiro, S and Castelo-Branco, M},
title = {SSVEP modulation via non-volitional neurofeedback: An in silico proof of concept.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a5},
pmid = {39569892},
issn = {1741-2552},
abstract = {Objective Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces (BCI) based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex. Approach Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex. Main Results Response models were obtained by analyzing, EEG data (n=8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the Steady-State Visual Evoked Potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controler's Linear, Time-Invariant (LTI) models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability. Significance In silico and in vivo data matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits. .},
}

@article {pmid39569866,
year = {2024},
author = {Thomson, CJ and Tully, TN and Stone, ES and Morrell, CB and Scheme, E and Warren, DJ and Hutchinson, DT and Clark, GA and George, JA},
title = {Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad94a7},
pmid = {39569866},
issn = {1741-2552},
abstract = {Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control. Approach: Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings. Main Results: Dataset aggregation reduced the root-mean-squared error of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets. Significance: Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.},
}

@article {pmid39567538,
year = {2024},
author = {Forenzo, D and Zhu, H and He, B},
title = {A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1256},
pmid = {39567538},
issn = {2052-4463},
support = {AT009263//U.S. Department of Health & Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; NS127849, NS096761, NS131069, NS124564//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; EB029354//U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Deep Learning ; Algorithms ; },
abstract = {This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography
*Deep Learning
Algorithms

@article {pmid39490524,
year = {2024},
author = {Pirelli, L and Grubb, KJ and George, I and Goldsweig, AM and Nazif, TM and Dahle, G and Myers, PO and Ouzounian, M and Szeto, WY and Maisano, F and Geirsson, A and Vahl, TP and Kodali, SK and Kaneko, T and Tang, GHL},
title = {The role of cardiac surgeons in transcatheter structural heart disease interventions: The evolution of cardiac surgery.},
journal = {The Journal of thoracic and cardiovascular surgery},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jtcvs.2024.10.037},
pmid = {39490524},
issn = {1097-685X},
}

@article {pmid39567330,
year = {2024},
author = {Pu, Y and Francks, C and Kong, XZ},
title = {Global brain asymmetry.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2024.10.008},
pmid = {39567330},
issn = {1879-307X},
abstract = {Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain.},
}

@article {pmid39567230,
year = {2024},
author = {Walters, GI and Foley, H and Huntley, CC and Naveed, A and Nettleton, K and Reilly, C and Thomas, M and Walker, C and Wheeler, K},
title = {Could a behaviour change intervention be used to address under-recognition of work-related asthma in primary care? a systematic review.},
journal = {BJGP open},
volume = {},
number = {},
pages = {},
doi = {10.3399/BJGPO.2024.0094},
pmid = {39567230},
issn = {2398-3795},
abstract = {BACKGROUND: Work-related asthma (WRA) is prevalent yet under-recognized in UK primary care.

AIM: We aimed to identify behaviour change interventions (BCI) intended for use in primary care to identify WRA, or any other chronic disease (that could be adapted for use in WRA).

DESIGN & SETTING: Systematic review METHOD: We searched CCRCT, Embase, PsychINFO and Ovid-MEDLINE databases (1946-2023) for studies describing development and/or evaluation of BCIs for case finding any chronic disease in primary care settings, aimed at either healthcare professionals and/or patients. Two blinded, independent reviewers screened abstracts and assessed full text articles. We undertook narrative synthesis for outcomes of usability and effectiveness, and for BCI development processes.

RESULTS: We included 14 studies from n=768 retrieved citations, comprising 3 randomised control trials, 1 uncontrolled experimental study, and 10 studies employing recognized multi-step BC methodologies. None of the studies were concerned with identification of asthma. BCIs had been developed for facilitating screening programmes (5), implementing guidelines (3) and individual case finding (6). Five studies measured effectiveness, in terms of screening adherence rates, pre-/post-intervention competency, satisfaction and usability, for clinicians, though none measured diagnostic rates.

CONCLUSION: No single or multi-component BCIs has been developed specifically to aid identification of asthma or WRA, though other chronic diseases have been targeted. Development has used BC methodologies that involved gathering data from a range of sources, and developing content specific to defined at-risk populations, so are not immediately transferable. Such methodologies could be used similarly to develop a primary acre-based BCI for WRA.},
}

@article {pmid39565521,
year = {2024},
author = {Guan, Z and Zhang, X and Huang, W and Li, K and Chen, D and Li, W and Sun, J and Chen, L and Mao, Y and Sun, H and Tang, X and Cao, L and Li, Y},
title = {A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39565521},
issn = {1995-8218},
abstract = {Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.},
}

@article {pmid39565505,
year = {2024},
author = {Wang, XN and Zhang, T and Han, BC and Luo, WW and Liu, WH and Yang, ZY and Disi, A and Sun, Y and Yang, JC},
title = {Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.},
journal = {Current medical science},
volume = {},
number = {},
pages = {},
pmid = {39565505},
issn = {2523-899X},
abstract = {OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.

METHODS: A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites. The neurofeedback group received active mu rhythm neurofeedback training, while the control group received a sham neurofeedback training. Other behavioral intervention programs were similar between the two groups.

RESULTS: After 60 sessions of treatment, both groups showed significant improvements in several domains including language, social and problem behavior. The neurofeedback group showed significantly greater improvements in expressive language (P=0.013) and cognitive awareness (including joint attention, P=0.003) than did the placebo-controlled group.

CONCLUSION: Artificial intelligence-powered wearable EEG neurofeedback, as a type of brain-computer interface application, is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.},
}

@article {pmid39561980,
year = {2024},
author = {Vacca, F and Galluzzi, F and Blanco-Formoso, M and Gianiorio, T and De Fazio, AF and Tantussi, F and Stürmer, S and Haq, W and Zrenner, E and Chaffiol, A and Joffrois, C and Picaud, S and Benfenati, F and De Angelis, F and Colombo, E},
title = {Solid-State Nanopores for Spatially Resolved Chemical Neuromodulation.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.4c02604},
pmid = {39561980},
issn = {1530-6992},
abstract = {Most neural prosthetic devices are based on electrical stimulation, although the modulation of neuronal activity by a localized chemical delivery would better mimic physiological synaptic machinery. In the past decade, various drug delivery approaches attempted to emulate synaptic transmission, although they were hampered by poor retention of their cargo while reaching the target destination, low spatial resolution, and poor biocompatibility and stability of the materials involved. Here, we propose a planar solid-state device for multisite neurotransmitter translocation at the nanoscale consisting of a nanopatterned ceramic membrane connected to a reservoir designed to store neurotransmitters. We achieved diffusion-mediated glutamate stimulation of primary neurons, while we showed the feasibility to translocate other molecules through the pores by either pressure or diffusion, proving the versatility of the proposed technology. Finally, the system proved to be a promising neuronal stimulation interface in mice and nonhuman primates ex vivo, paving the way toward a biomimetic chemical stimulation in neural prosthetics and brain machine interfaces.},
}

@article {pmid39560446,
year = {2024},
author = {Xu, F and Shi, W and Lv, C and Sun, Y and Guo, S and Feng, C and Zhang, Y and Jung, TP and Leng, J},
title = {Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2450069},
doi = {10.1142/S0129065724500692},
pmid = {39560446},
issn = {1793-6462},
abstract = {Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals from stroke patients poses challenges. To address the issues of low accuracy and efficiency in EEG classification, particularly involving MI, the study proposes a residual graph convolutional network (M-ResGCN) framework based on the modified S-transform (MST), and introduces the self-attention mechanism into residual graph convolutional network (ResGCN). This study uses MST to extract EEG time-frequency domain features, derives spatial EEG features by calculating the absolute Pearson correlation coefficient (aPcc) between channels, and devises a method to construct the adjacency matrix of the brain network using aPcc to measure the strength of the connection between channels. Experimental results involving 16 stroke patients and 16 healthy subjects demonstrate significant improvements in classification quality and robustness across tests and subjects. The highest classification accuracy reached 94.91% and a Kappa coefficient of 0.8918. The average accuracy and F1 scores from 10 times 10-fold cross-validation are 94.38% and 94.36%, respectively. By validating the feasibility and applicability of brain networks constructed using the aPcc in EEG signal analysis and feature encoding, it was established that the aPcc effectively reflects overall brain activity. The proposed method presents a novel approach to exploring channel relationships in MI-EEG and improving classification performance. It holds promise for real-time applications in MI-based BCI systems.},
}

@article {pmid39560167,
year = {2024},
author = {Shah, AM},
title = {Hopeful progress in artificial vision.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.14912},
pmid = {39560167},
issn = {1525-1594},
abstract = {Visual impairment has been augmented by glasses for centuries. With the advent of newer technologies, correction of more severe visual impairment may be possible with brain-computer interface and eye implants.},
}

@article {pmid39557568,
year = {2024},
author = {Harris Caceres, A and Barany, DA and Dundon, NM and Smith, J and Marneweck, M},
title = {Neural encoding of direction and distance across reference frames in visually guided reaching.},
journal = {eNeuro},
volume = {},
number = {},
pages = {},
doi = {10.1523/ENEURO.0405-24.2024},
pmid = {39557568},
issn = {2373-2822},
abstract = {Goal-directed actions require transforming sensory information into motor plans defined across multiple parameters and reference frames. Substantial evidence supports the encoding of target direction in gaze- and body-centered coordinates within parietal and premotor regions. However, how the brain encodes the equally critical parameter of target distance remains less understood. Here, using Bayesian pattern component modeling of fMRI data during a delayed reach-to-target task, we dissociated the neural encoding of both target direction and the relative distances between target, gaze, and hand at early and late stages of motor planning. This approach revealed independent representations of direction and distance along the human dorsomedial reach pathway. During early planning, most premotor and superior parietal areas encoded a target's distance in single or multiple reference frames and encoded its direction. In contrast, distance encoding was magnified in gaze- and body-centric reference frames during late planning. These results emphasize a flexible and efficient human central nervous system that achieves goals by remapping sensory information related to multiple parameters, such as distance and direction, in the same brain areas.Significance statement Motor plans specify various parameters, e.g., target direction and distance, each of which can be defined in multiple reference frames relative to gaze, limb, or head. Combining fMRI, a delayed reach-to-target task, and Bayesian pattern component modeling, we present evidence for independent goal-relevant representations of direction and distance in multiple reference frames across early and late planning along the dorsomedial reach pathway. Initially, areas encoding distance also encode direction, but later in planning, distance encoding in multiple reference frames was magnified. These results emphasize central nervous system flexibility in transforming movement parameters in multiple reference frames crucial for successful goal-directed actions and have important implications for brain-computer interface technology advances with sensory integration.},
}

@article {pmid39426071,
year = {2024},
author = {Chowdhury, RS and Bose, S and Ghosh, S and Konar, A},
title = {Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {183},
number = {},
pages = {109260},
doi = {10.1016/j.compbiomed.2024.109260},
pmid = {39426071},
issn = {1879-0534},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; },
abstract = {In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier's performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.},
}

Humans
*Deep Learning
*Electroencephalography/methods
Signal Processing, Computer-Assisted
Imagination/physiology
Brain/physiology
Neural Networks, Computer
Brain-Computer Interfaces
Attention/physiology

@article {pmid39556950,
year = {2024},
author = {Kılınç Bülbül, D and Walston, ST and Duvan, FT and Garrido, JA and Guclu, B},
title = {Decoding sensorimotor information from somatosensory cortex by flexible epicortical μECoG arrays in unrestrained behaving rats.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9405},
pmid = {39556950},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25 μm).

APPROACH: Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies. μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.

MAIN RESULTS: Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.

SIGNIFICANCE: Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by using μECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.},
}

@article {pmid39556340,
year = {2024},
author = {Sang, Y and Li, B and Su, T and Zhan, H and Xiong, Y and Huang, Z and Wang, C and Cong, X and Du, M and Wu, Y and Yu, H and Yang, X and Ding, K and Wang, X and Miao, X and Gong, W and Wang, L and Zhao, J and Zhou, Y and Liu, W and Hu, X and Sun, Q},
title = {Visualizing ER-phagy and ER architecture in vivo.},
journal = {The Journal of cell biology},
volume = {223},
number = {12},
pages = {},
doi = {10.1083/jcb.202408061},
pmid = {39556340},
issn = {1540-8140},
support = {32025012//National Natural Science Foundation/ ; 2021YFC2700901//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {Animals ; *Mice, Transgenic ; *Endoplasmic Reticulum/metabolism/genetics ; Mice ; Green Fluorescent Proteins/metabolism/genetics ; Luminescent Proteins/genetics/metabolism ; Red Fluorescent Protein ; Genes, Reporter ; Mice, Inbred C57BL ; },
abstract = {ER-phagy is an evolutionarily conserved mechanism crucial for maintaining cellular homeostasis. However, significant gaps persist in our understanding of how ER-phagy and the ER network vary across cell subtypes, tissues, and organs. Furthermore, the pathophysiological relevance of ER-phagy remains poorly elucidated. Addressing these questions requires developing quantifiable methods to visualize ER-phagy and ER architecture in vivo. We generated two transgenic mouse lines expressing an ER lumen-targeting tandem RFP-GFP (ER-TRG) tag, either constitutively or conditionally. This approach enables precise spatiotemporal measurements of ER-phagy and ER structure at single-cell resolution in vivo. Systemic analysis across diverse organs, tissues, and primary cultures derived from these ER-phagy reporter mice unveiled significant variations in basal ER-phagy, both in vivo and ex vivo. Furthermore, our investigation uncovered substantial remodeling of ER-phagy and the ER network in different tissues under stressed conditions such as starvation, oncogenic transformation, and tissue injury. In summary, both reporter models represent valuable resources with broad applications in fundamental research and translational studies.},
}

Animals
*Mice, Transgenic
*Endoplasmic Reticulum/metabolism/genetics
Mice
Green Fluorescent Proteins/metabolism/genetics
Luminescent Proteins/genetics/metabolism
Red Fluorescent Protein
Genes, Reporter
Mice, Inbred C57BL

@article {pmid39555298,
year = {2024},
author = {Li, J and Shi, W and Li, Y},
title = {An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2689-2707},
pmid = {39555298},
issn = {1871-4080},
abstract = {Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.},
}

@article {pmid39555297,
year = {2024},
author = {Yin, Y and Kong, W and Tang, J and Li, J and Babiloni, F},
title = {PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2883-2896},
pmid = {39555297},
issn = {1871-4080},
abstract = {Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.},
}

@article {pmid39555294,
year = {2024},
author = {Leng, J and Yu, X and Wang, C and Zhao, J and Zhu, J and Chen, X and Zhu, Z and Jiang, X and Zhao, J and Feng, C and Yang, Q and Li, J and Jiang, L and Xu, F and Zhang, Y},
title = {Functional connectivity of EEG motor rhythms after spinal cord injury.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {3015-3029},
pmid = {39555294},
issn = {1871-4080},
abstract = {Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.},
}

@article {pmid39555292,
year = {2024},
author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y},
title = {Affective EEG-based cross-session person identification using hierarchical graph embedding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2897-2908},
pmid = {39555292},
issn = {1871-4080},
abstract = {The electroencephalogram (EEG) signal is being investigated as a more confidential biometric for person identification. Despite recent advancements, a persistent challenge lies in the influence of variations in affective states. Affective states consistently exist during data collection, regardless of the protocol used. Additionally, the inherently non-stationary nature of EEG makes it susceptible to fluctuations in affective states over time. Therefore, it would be highly crucial to perform precise EEG-based person identification under varying affective states. This paper employed an integrated Multi-scale Convolution and Graph Pooling network (MCGP) to mitigate the impact of affective state variations. MCGP utilized multiple 1D convolutions at different scales to dynamically extract and fuse features. Additionally, a graph pooling layer with an attention mechanism was incorporated to generate hierarchical graph embeddings. These embeddings were concatenated as inputs for a fully connected classification layer. Experiments were conducted on the SEED and SEED-V dataset, revealing that MCGP achieved an average accuracy of 85.51% for SEED and 88.69% for SEED-V in cross-session conditions involving mixed affective states. Under single affective state cross-session scenario, MCGP achieved an average accuracy of 85.75% for SEED and 88.06% for SEED-V for the same affective states, while obtaining 79.57% for SEED and 84.52% for SEED-V for different affective states. Results indicated that, compared to the baseline methods, MCGP effectively mitigated the impact of variations in affective states across different sessions. In single affective state cross-session scenario, identification performance for the same affective states was slightly higher than that for different affective states.},
}

@article {pmid39555282,
year = {2024},
author = {Chen, L and Gao, H and Wang, Z and Gu, B and Zhou, W and Pang, M and Zhang, K and Liu, X and Ming, D},
title = {Vagus nerve electrical stimulation in the recovery of upper limb motor functional impairment after ischemic stroke.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {3107-3124},
pmid = {39555282},
issn = {1871-4080},
abstract = {Ischemic stroke (IS) is characterized by high mortality, disability rates, and a high risk of recurrence. Motor dysfunction, such as limb hemiparesis, dysphagia, auditory disorders, and speech disorders, usually persists after stroke, which imposes a heavy burden on society and the health care system. Traditional rehabilitation therapies may be ineffective in promoting functional recovery after stroke, and alternative strategies are urgently needed. The Food and Drug Administration (FDA) has approved invasive vagus nerve stimulation (iVNS) for the improvement of refractory epilepsy, treatment-resistant depression, obesity, and moderate to severe upper limb motor impairment following chronic ischemic stroke. Additionally, the FDA has approved transcutaneous vagus nerve stimulation (tVNS) for the improvement of cluster headaches and acute migraines. Recent studies have demonstrated that vagus nerve stimulation (VNS) has neuroprotective effects in both transient and permanent cerebral ischemia animal models, significantly improving upper limb motor impairments, auditory deficits, and swallowing difficulties. Firstly, this article reviews two potential neuronal death pathways following IS, including autophagy and inflammatory responses. Then delves into the current status of preclinical and clinical research on the functional recovery following IS with VNS, as well as the potential mechanisms mediating its neuroprotective effects. Finally, the optimal parameters and timing of VNS application are summarized, and the future challenges and directions of VNS in the treatment of IS are discussed. The application of VNS in stroke rehabilitation research has reached a critical stage, and determining how to safely and effectively translate this technology into clinical practice is of utmost importance. Further preclinical and clinical studies are needed to elucidate the therapeutic mechanisms of VNS.},
}

@article {pmid39555271,
year = {2024},
author = {Liu, Y and Yu, S and Li, J and Ma, J and Wang, F and Sun, S and Yao, D and Xu, P and Zhang, T},
title = {Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2455-2470},
pmid = {39555271},
issn = {1871-4080},
abstract = {UNLABELLED: Motor imagery (MI) is a high-level cognitive process that has been widely applied to brain-computer inference (BCI) and motor recovery. In practical applications, however, huge individual differences and unclear neural mechanisms have seriously hindered the application of MI and BCI systems. Thus, it is urgently needed to explore MI from a new perspective. Here, we applied a hidden Markov model (HMM) to explore the dynamic organization patterns of left- and right-hand MI tasks. Eleven distinct HMM states were identified based on MI-related EEG data. We found that these states can be divided into three metastates by clustering analysis, showing a highly organized structure. We also assessed the probability activation of each HMM state across time. The results showed that the state probability activation of task-evoked have similar trends to that of event-related desynchronization/synchronization (ERD/ERS). By comparing the differences in temporal features of HMM states between left- and right-hand MI, we found notable variations in fractional occupancy, mean life time, mean interval time, and transition probability matrix across stages and states. Interestingly, we found that HMM states activated in the left occipital lobe had higher occupancy during the left-hand MI task, and conversely, during the right-hand MI task, HMM states activated in the right occipital lobe had higher occupancy. Moreover, significant correlations were observed between BCI performance and features of HMM states. Taken together, our findings explored dynamic networks underlying the MI-related process and provided a complementary understanding of different MI tasks, which may contribute to improving the MI-BCI systems.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10099-9.},
}

@article {pmid39555270,
year = {2024},
author = {Wang, Y and Zhang, M and Li, M and Cui, H and Chen, X},
title = {Development of a humanoid robot control system based on AR-BCI and SLAM navigation.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2857-2870},
pmid = {39555270},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation. An 8-target steady-state visual evoked potential (SSVEP)-based BCI was implemented to enable direct control of the humanoid robot by the user. A Microsoft HoloLens was utilized to display visual stimuli for eliciting SSVEPs. Filter bank canonical correlation analysis (FBCCA), a training-free method, was used to detect SSVEPs in this study. By leveraging SLAM technology, the proposed system alleviates the need for frequent control commands transmission from the user, thereby effectively reducing their workload. Online results from 12 healthy subjects showed this developed BCI system was able to select a command out of eight potential targets with an average accuracy of 94.79%. The autonomous navigation subsystem enabled the humanoid robot to autonomously navigate to a destination chosen utilizing the proposed BCI. Furthermore, all participants successfully completed the experimental task using the developed system without any prior training. These findings illustrate the feasibility of the developed system and its potential to contribute novel insights into humanoid robots control strategies.},
}

@article {pmid39555269,
year = {2024},
author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X},
title = {Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2871-2881},
pmid = {39555269},
issn = {1871-4080},
abstract = {With the continuing development of brain-computer interface technology, the analysis and interpretation of brain signals are becoming increasingly important. In the field of brain-computer interfaces, motor imagery (MI) is an important paradigm for generating specific brain signals through thought alone, rather than actual movement, for computer decoding. Functional near-infrared spectroscopy (fNIRS) imaging technology has been increasingly used in brain-computer interfaces due to its advantages of non-invasiveness, low resource requirements, low cost, and high spatial resolution. Scientists have done a lot of work in channel selection, feature selection, and then applying traditional machine learning methods for classification, but the results achieved so far are still insufficient to meet the conditions for realizing fNIRS brain-computer interfaces. To achieve a higher level of classification of fNIRS signals, we propose a method that fuses CNN and attention mechanisms to analyze the near-infrared signals of motor imagery and mental arithmetic data, which is fed into a neural network by deriving signals of changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations through the modified Beer-Lambert law, and then applied to the fNIRS dataset of 29 healthy subjects to validate the proposed method. In the fNIRS-based BCI, the average classification accuracy of the MI signal from HbR and HbO reaches 85.92% and 86.21%, respectively, and the average classification accuracy of the MA signal reaches 89.66% and 88.79%, respectively. The advantage of our approach is that it is lightweight and improves the classification accuracy of current BCI fNIRS signals.},
}

@article {pmid39555266,
year = {2024},
author = {Wang, Z and Song, X and Chen, L and Nan, J and Sun, Y and Pang, M and Zhang, K and Liu, X and Ming, D},
title = {Research progress of epileptic seizure prediction methods based on EEG.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2731-2750},
pmid = {39555266},
issn = {1871-4080},
abstract = {At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.},
}

@article {pmid39555257,
year = {2024},
author = {Ma, J and Yang, B and Rong, F and Gao, S and Wang, W},
title = {Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2521-2534},
pmid = {39555257},
issn = {1871-4080},
abstract = {Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.},
}

@article {pmid39555252,
year = {2024},
author = {Pan, Y and Li, N and Zhang, Y and Xu, P and Yao, D},
title = {Short-length SSVEP data extension by a novel generative adversarial networks based framework.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2925-2945},
pmid = {39555252},
issn = {1871-4080},
abstract = {Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.},
}

@article {pmid39554850,
year = {2024},
author = {Dan, Y and Zhou, D and Wang, Z},
title = {Discriminative possibilistic clustering promoting cross-domain emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1458815},
pmid = {39554850},
issn = {1662-4548},
abstract = {The affective Brain-Computer Interface (aBCI) systems strive to enhance prediction accuracy for individual subjects by leveraging data from multiple subjects. However, significant differences in EEG (Electroencephalogram) feature patterns among subjects often hinder these systems from achieving the desired outcomes. Although studies have attempted to address this challenge using subject-specific classifier strategies, the scarcity of labeled data remains a major hurdle. In light of this, Domain Adaptation (DA) technology has gradually emerged as a prominent approach in the field of EEG-based emotion recognition, attracting widespread research interest. The crux of DA learning lies in resolving the issue of distribution mismatch between training and testing datasets, which has become a focal point of academic attention. Currently, mainstream DA methods primarily focus on mitigating domain distribution discrepancies by minimizing the Maximum Mean Discrepancy (MMD) or its variants. Nevertheless, the presence of noisy samples in datasets can lead to pronounced shifts in domain means, thereby impairing the adaptive performance of DA methods based on MMD and its variants in practical applications to some extent. Research has revealed that the traditional MMD metric can be transformed into a 1-center clustering problem, and the possibility clustering model is adept at mitigating noise interference during the data clustering process. Consequently, the conventional MMD metric can be further relaxed into a possibilistic clustering model. Therefore, we construct a distributed distance measure with Discriminative Possibilistic Clustering criterion (DPC), which aims to achieve two objectives: (1) ensuring the discriminative effectiveness of domain distribution alignment by finding a shared subspace that minimizes the overall distribution distance between domains while maximizing the semantic distribution distance according to the principle of "sames attract and opposites repel"; and (2) enhancing the robustness of distribution distance measure by introducing a fuzzy entropy regularization term. Theoretical analysis confirms that the proposed DPC is an upper bound of the existing MMD metric under certain conditions. Therefore, the MMD objective can be effectively optimized by minimizing the DPC. Finally, we propose a domain adaptation in Emotion recognition based on DPC (EDPC) that introduces a graph Laplacian matrix to preserve the geometric structural consistency between data within the source and target domains, thereby enhancing label propagation performance. Simultaneously, by maximizing the use of source domain discriminative information to minimize domain discrimination errors, the generalization performance of the DA model is further improved. Comparative experiments on several representative domain adaptation learning methods using multiple EEG datasets (i.e., SEED and SEED-IV) show that, in most cases, the proposed method exhibits better or comparable consistent generalization performance.},
}

@article {pmid39554511,
year = {2024},
author = {Herbozo Contreras, LF and Truong, ND and Eshraghian, JK and Xu, Z and Huang, Z and Bersani-Veroni, TV and Aguilar, I and Leung, WH and Nikpour, A and Kavehei, O},
title = {Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.},
journal = {PNAS nexus},
volume = {3},
number = {11},
pages = {pgae488},
pmid = {39554511},
issn = {2752-6542},
abstract = {Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.},
}

@article {pmid39553842,
year = {2024},
author = {Zhou, L and Wu, B and Qin, B and Gao, F and Li, W and Hu, H and Zhu, Q and Qian, Z},
title = {Cortico-muscular coherence of time-frequency and spatial characteristics under movement observation, movement execution, and movement imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {3},
pages = {1079-1096},
pmid = {39553842},
issn = {1871-4080},
abstract = {Studies show that movement observation (MO), movement imagery (MI), or movement execution (ME) based brain-computer interface systems are promising in promoting the rehabilitation and reorganization of damaged motor function. This study was aimed to explore and compare the motor function rehabilitation mechanism among MO, MI, and ME. 64-channel electroencephalogram and 4-channel electromyogram data were collected from 39 healthy participants (25 males, 14 females; 18-23 years old) during MO, ME, and MI. We analyzed and compared the inter-cortical, inter-muscular, cortico-muscular, and spatial coherence under MO, ME, and MI. Under MO, ME, and MI, cortico-muscular coherence was strongest at the beta-lh band, which means the beta frequency band for cortical signals and the lh frequency band for muscular signals. 56.25-96.88% of the coherence coefficients were significantly larger than 0.5 (ps < 0.05) at the beta-lh band. MO and ME had a contralateral advantage in the spatial coherence between cortex and muscle, while MI had an ipsilateral advantage in the spatial coherence between cortex and muscle. Our results show that the cortico-muscular beta-lh band plays a critical role in the synchronous coupling between cortex and muscle. Also, our findings suggest that the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and premotor cortex (PMC) are the specific regions of MO, ME, and MI. However, their pathways of regulating muscles are different under MO, ME, and MI. This study is important for better understanding the motor function rehabilitation mechanism in MO, MI, and ME.},
}

@article {pmid39551888,
year = {2024},
author = {Lin, RR and Zhang, K},
title = {Survey of real-time brainmedia in artistic exploration.},
journal = {Visual computing for industry, biomedicine, and art},
volume = {7},
number = {1},
pages = {27},
pmid = {39551888},
issn = {2524-4442},
support = {2021JC02G114//Grant #2021JC02G114./ ; },
abstract = {This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.},
}

@article {pmid39551818,
year = {2024},
author = {Zhao, H and Zhang, C and Tao, R and Wang, M and Yin, Y and Xu, S},
title = {Dyadic Similarity in Social Value Orientation Modulates Hyper-Brain Network Dynamics During Interpersonal Coordination: An fNIRS-Based Hyperscanning Study.},
journal = {Brain topography},
volume = {38},
number = {1},
pages = {15},
pmid = {39551818},
issn = {1573-6792},
support = {23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 72171151//National Natural Science Foundation of China/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; },
mesh = {Humans ; Male ; Young Adult ; Female ; *Spectroscopy, Near-Infrared/methods ; *Brain/physiology ; *Interpersonal Relations ; Cooperative Behavior ; Adult ; Brain Mapping/methods ; Social Behavior ; Neural Pathways/physiology ; },
abstract = {As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode.},
}

Humans
Male
Young Adult
Female
*Spectroscopy, Near-Infrared/methods
*Brain/physiology
*Interpersonal Relations
Cooperative Behavior
Adult
Brain Mapping/methods
Social Behavior
Neural Pathways/physiology

@article {pmid39551270,
year = {2024},
author = {Xiang, L and Zhao, Y and Li, X and Shi, R and Wen, Z and Xu, X and Hu, Y and Xu, Q and Chen, Y and Ma, J and Shen, W},
title = {Astrocytic calcium signals modulate exercise-induced fatigue in mice.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2024.11.033},
pmid = {39551270},
issn = {1873-7544},
abstract = {Exercise-induced fatigue (EF) is characterized by a decline in maximal voluntary muscle force following prolonged physical activity, influenced by both peripheral and central factors. Central fatigue involves complex interactions within the central nervous system (CNS), where astrocytes play a crucial role. This study explores the impact of astrocytic calcium signals on EF. We used adeno-associated viruses to express GCaMP7b in astrocytes of the dorsal striatum in mice, allowing us to monitor calcium dynamics. Our findings reveal that EF significantly increases the frequency of spontaneous astrocytic calcium signals. Utilizing genetic tools to either enhance or reduce astrocytic calcium signaling, we observed corresponding decreases and increases in exercise-induced fatigue time, respectively. Furthermore, modulation of astrocytic calcium signals influenced corticostriatal synaptic plasticity, with increased signals impairing and decreased signals ameliorating long-term depression (LTD). These results highlight the pivotal role of astrocytic calcium signaling in the regulation of exercise-induced fatigue and synaptic plasticity in the striatum.},
}

@article {pmid39550056,
year = {2024},
author = {Chen, Y and Bai, J and Shi, N and Jiang, Y and Chen, X and Ku, Y and Gao, X},
title = {Intermodulation Frequency Components in Steady-State Visual Evoked Potentials: Generation, Characteristics and Applications.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {120937},
doi = {10.1016/j.neuroimage.2024.120937},
pmid = {39550056},
issn = {1095-9572},
abstract = {The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.},
}

@article {pmid39549531,
year = {2024},
author = {Imtiaz, MN and Khan, N},
title = {Enhanced cross-dataset electroencephalogram-based emotion recognition using unsupervised domain adaptation.},
journal = {Computers in biology and medicine},
volume = {184},
number = {},
pages = {109394},
doi = {10.1016/j.compbiomed.2024.109394},
pmid = {39549531},
issn = {1879-0534},
abstract = {Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain-computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) signals among individuals limit the cross-domain application of EEG-based emotion recognition models. Addressing cross-dataset scenarios poses greater challenges due to changes in subject demographics, recording devices, and stimuli presented. To tackle these challenges, we propose an improved method for classifying EEG-based emotions across domains with different distributions. We propose a Gradual Proximity-guided Target Data Selection (GPTDS) technique, which gradually selects reliable target domain samples for training based on their proximity to the source clusters and the model's confidence in predicting them. This approach avoids negative transfer caused by diverse and unreliable samples. Additionally, we introduce a cost-effective test-time augmentation (TTA) technique named Prediction Confidence-aware Test-Time Augmentation (PC-TTA). Traditional TTA methods often face substantial computational burden, limiting their practical utility. By applying TTA only when necessary, based on the model's predictive confidence, our approach improves the model's performance during inference while minimizing computational costs compared to traditional TTA approaches. Experiments on the DEAP and SEED datasets demonstrate that our method outperforms state-of-the-art approaches, achieving accuracies of 67.44% when trained on DEAP and tested on SEED, and 59.68% vice versa, with improvements of 7.09% and 6.07% over the baseline. It excels in detecting both positive and negative emotions, highlighting its effectiveness for practical emotion recognition in healthcare applications. Moreover, our proposed PC-TTA technique reduces computational time by a factor of 15 compared to traditional full TTA approaches.},
}

@article {pmid39549493,
year = {2024},
author = {Hu, P and Zhang, X and Li, M and Zhu, Y and Shi, L},
title = {TSOM: Small object motion detection neural network inspired by avian visual circuit.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106881},
doi = {10.1016/j.neunet.2024.106881},
pmid = {39549493},
issn = {1879-2782},
abstract = {Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial-temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.},
}

@article {pmid39549492,
year = {2024},
author = {Borra, D and Magosso, E and Ravanelli, M},
title = {A protocol for trustworthy EEG decoding with neural networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106847},
doi = {10.1016/j.neunet.2024.106847},
pmid = {39549492},
issn = {1879-2782},
abstract = {Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is rarely performed and limited to network-related hyperparameters. Moreover, pipelines are highly sensitive to performance fluctuations due to random initialization, hindering their reliability. Here, we design a comprehensive protocol for EEG decoding that explores the hyperparameters characterizing the entire pipeline and that includes multi-seed initialization for providing robust performance estimates. Our protocol is validated on 9 datasets about motor imagery, P300, SSVEP, including 204 participants and 26 recording sessions, and on different deep learning models. We accompany our protocol with extensive experiments on the main aspects influencing it, such as the number of participants used for hyperparameter search, the split into sequential simpler searches (multi-step search), the use of informed vs. non-informed search algorithms, and the number of random seeds for obtaining stable performance. The best protocol included 2-step hyperparameter search via an informed search algorithm, with the final training and evaluation performed using 10 random initializations. The optimal trade-off between performance and computational time was achieved by using a subset of 3-5 participants for hyperparameter search. Our protocol consistently outperformed baseline state-of-the-art pipelines, widely across datasets and models, and could represent a standard approach for neuroscientists for decoding EEG in a trustworthy and reliable way.},
}

@article {pmid39548722,
year = {2024},
author = {Farahmand, M and Mousavi, M and Azizi, F and Ramezani Tehrani, F},
title = {Exploring the Influence of Age at Menarche on Metabolic Syndrome and Its Components Across Different Women's Birth Cohorts.},
journal = {Endocrinology, diabetes & metabolism},
volume = {7},
number = {6},
pages = {e70015},
doi = {10.1002/edm2.70015},
pmid = {39548722},
issn = {2398-9238},
support = {43002333//Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences/ ; },
mesh = {Humans ; *Menarche/physiology ; Female ; *Metabolic Syndrome/epidemiology/etiology ; Cross-Sectional Studies ; Adult ; Adolescent ; Iran/epidemiology ; Age Factors ; Middle Aged ; Child ; Birth Cohort ; Young Adult ; Risk Factors ; Prevalence ; Cohort Studies ; },
abstract = {PURPOSE: Metabolic syndrome (MetS) is the primary cardiovascular risk factor, making it a global issue. Our objective was to assess the association between the age at menarche (AAM) and MetS and its components in different generations of women.

METHODS: In this cross-sectional study, 5500 eligible women aged ≥ 20 who participated in the Tehran lipid and glucose study in 2015-2017 were selected. Participants were divided into groups by birth cohorts (BC) (born ≤ 1959, 1960-1979, and ≥ 1980) and AAM (≤ 11, 12-15, and ≥ 16 years, early, normal, and late, respectively). The status of MetS and its components were compared amongst participants using logistic regression.

RESULTS: Normal AAM (12-15 years) was considered the reference group. The adjusted model revealed that AAM ≤ 11 is associated with a higher risk of 34% (95% confidence interval (CI): 1.04, 1.71) in MetS, and the prevalence of MetS in the early menarche group was higher in BCI, and BCII (odds ratio (OR): 1.87; 95% CI: 1.04, 3.36 and OR: 1.33; 95% CI: 1.00, 1.89, respectively). Those with late menarche demonstrated a lower risk (OR:0.72; 95% CI: 0.57, 0.91) of abdominal obesity, and early menarche showed a higher risk (OR: 1.45; CI: 1.14, 1.86). This higher risk in early menarche was observed in BCI and BCII (OR: 1.76; 95% CI: 1.16, 2.66 and OR: 1.80; 95% CI: 1.23, 2.64, respectively). However, the protective effect of late menarche was observed in BC II and BC III (OR: 0.74; 95% CI: 0.54, 1.00 and OR: 0.64; 95% CI: 0.44, 0.96, respectively).

CONCLUSIONS: The influential effect of AAM on metabolic disturbances varies amongst different generations.},
}

Humans
*Menarche/physiology
Female
*Metabolic Syndrome/epidemiology/etiology
Cross-Sectional Studies
Adult
Adolescent
Iran/epidemiology
Age Factors
Middle Aged
Child
Birth Cohort
Young Adult
Risk Factors
Prevalence
Cohort Studies

@article {pmid39548177,
year = {2024},
author = {Ma, J and Ma, W and Zhang, J and Li, Y and Yang, B and Shan, C},
title = {Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {28170},
pmid = {39548177},
issn = {2045-2322},
support = {ZRJY2021-QM02//National High Level Hospital Clinical Research Funding and Elite Medical Professionals Project of China-Japan Friendship Hospital/ ; BYESS2023173//Young Elite Scientist Sponsorship Program By Bast/ ; 82272612//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Neural Networks, Computer ; Algorithms ; Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Adult ; Machine Learning ; },
abstract = {The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Stroke/physiopathology
*Neural Networks, Computer
Algorithms
Stroke Rehabilitation/methods
Male
Female
Middle Aged
Adult
Machine Learning

@article {pmid39547137,
year = {2024},
author = {Wellman, SM and Forrest, AM and Douglas, MM and Subbaraman, A and Zhang, G and Kozai, TDY},
title = {Dynamic changes in the structure and function of brain mural cells around chronically implanted microelectrodes.},
journal = {Biomaterials},
volume = {315},
number = {},
pages = {122963},
doi = {10.1016/j.biomaterials.2024.122963},
pmid = {39547137},
issn = {1878-5905},
abstract = {Integration of neural interfaces with minimal tissue disruption in the brain is ideal to develop robust tools that can address essential neuroscience questions and combat neurological disorders. However, implantation of intracortical devices provokes severe tissue inflammation within the brain, which requires a high metabolic demand to support a complex series of cellular events mediating tissue degeneration and wound healing. Pericytes, peri-vascular cells involved in blood-brain barrier maintenance, vascular permeability, waste clearance, and angiogenesis, have recently been implicated as potential perpetuators of neurodegeneration in brain injury and disease. While the intimate relationship between pericytes and the cortical microvasculature have been explored in other disease states, their behavior following microelectrode implantation, which is responsible for direct blood vessel disruption and dysfunction, is currently unknown. Using two-photon microscopy we observed dynamic changes in the structure and function of pericytes during implantation of a microelectrode array over a 4-week implantation period. Pericytes respond to electrode insertion through transient increases in intracellular calcium and underlying constriction of capillary vessels. Within days following the initial insertion, we observed an influx of new, proliferating pericytes which contribute to new blood vessel formation. Additionally, we discovered a potentially novel population of reactive immune cells in close proximity to the electrode-tissue interface actively engaging in encapsulation of the microelectrode array. Finally, we determined that intracellular pericyte calcium can be modulated by intracortical microstimulation in an amplitude- and frequency-dependent manner. This study provides a new perspective on the complex biological sequelae occurring at the electrode-tissue interface and will foster new avenues of potential research consideration and lead to development of more advanced therapeutic interventions towards improving the biocompatibility of neural electrode technology.},
}

@article {pmid39545725,
year = {2024},
author = {Noneman, KK and Patrick Mayo, J},
title = {Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2450070},
doi = {10.1142/S0129065724500709},
pmid = {39545725},
issn = {1793-6462},
abstract = {Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.},
}

@article {pmid39545148,
year = {2024},
author = {Wohns, N and Dorfman, N and Klein, E},
title = {Caregivers in implantable brain-computer interface research: a scoping review.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1490066},
pmid = {39545148},
issn = {1662-5161},
abstract = {INTRODUCTION: While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention.

OBJECTIVES: This report investigates the extent to which caregivers are mentioned in publications describing implantable BCI (iBCI) research for individuals with motor dysfunction, communication impairment, and blindness.

METHODS: The scoping review was conducted in June 2024 using the PubMed and Web of Science bibliographic databases. The articles were systematically searched using query terms for caregivers, family members, and guardians, and the results were quantitatively and qualitatively analyzed.

RESULTS: Our search yielded 315 unique studies, 78 of which were included in this scoping review. Thirty-four (43.6%) of the 78 articles mentioned the study participant's caregivers. We sorted these into 5 categories: Twenty-two (64.7%) of the 34 articles thanked caregivers in the acknowledgement section, 6 (17.6%) articles described the caregiver's role with regard to the consent process, 12 (35.3%) described the caregiver's role in the technical maintenance and upkeep of the BCI system or in other procedural aspects of the study, 9 (26.5%) discussed how the BCI enhanced participant communication and goal-directed behavior with the help of a caregiver, and 3 (8.8%) articles included general comments that did not fit into the other categories but still related to the importance of caregivers in the lives of the research participants.

DISCUSSION: Caregivers were mentioned in less than half of BCI studies in this review. The studies that offered more robust discussions of caregivers provide valuable insight into the integral role that caregivers play in supporting the study participants and the research process. Attention to the role of caregivers in successful BCI research studies can help guide the responsible development of future BCI study protocols.},
}

@article {pmid39545146,
year = {2024},
author = {Wu, X and Ju, X and Dai, S and Li, X and Li, M},
title = {Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1464431},
pmid = {39545146},
issn = {1662-5161},
abstract = {BACKGROUND: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance.

METHOD: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model.

RESULT: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively.

CONCLUSION: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.},
}

@article {pmid39544330,
year = {2024},
author = {Wang, HZ and Saeed, S and Zhang, JY and Hu, SH},
title = {Bridging Three Years of Insights: Examining the Association Between Depression and Gallstone Disease.},
journal = {Journal of clinical medicine research},
volume = {16},
number = {10},
pages = {472-482},
pmid = {39544330},
issn = {1918-3003},
abstract = {BACKGROUND: Despite sharing common pathophysiological risk factors, the relationship between gallstones and depression requires further evidence for a clearer understanding. This study combines the National Health and Nutrition Examination Survey 2017 - 2020 observational data and Mendelian randomization (MR) analysis to shed light on the potential correlation between these conditions.

METHODS: By analyzing the National Health and Nutrition Examination Survey 2017 - 2020 data through weighted multivariable-adjusted logistic regression, we examined the association between depression and gallstone risk. MR was subsequently applied, utilizing genetic instruments from a large genome-wide association study on depression (excluding 23andMe, 500,199 participants) and gallstone data (28,627 cases, 348,373 controls), employing the main inverse variance-weighted method alongside other MR methods to explore the causal relationship. Sensitivity analyses validated the study's conclusions.

RESULTS: Among the 5,303 National Health and Nutrition Examination Survey participants, a significant association was found between depressive symptoms and increased gallstone risk (initial odds ratio (OR) = 2.001; 95% confidence interval (CI) = 1.523 - 2.598; P < 0.001), with the association persisting after comprehensive adjustments (final OR = 1.687; 95% CI = 1.261 - 2.234; P < 0.001). MR findings also indicated a causal link between genetically predicted depression and higher gallstone risk (OR = 1.164; 95% CI = 1.053 - 1.286; P = 0.003).

CONCLUSIONS: Depression is significantly associated with a higher risk of gallstones, supported by genetic evidence suggesting a causal link. These findings highlight the importance of considering depression in gallstone risk assessments and management strategies.},
}

@article {pmid39543314,
year = {2024},
author = {Rybář, M and Poli, R and Daly, I},
title = {Using data from cue presentations results in grossly overestimating semantic BCI performance.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {28003},
pmid = {39543314},
issn = {2045-2322},
abstract = {Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.},
}

@article {pmid38888742,
year = {2024},
author = {Bérubé, M and Verret, M and Bourque, L and Côté, C and Guénette, L and Richard-Denis, A and Ouellet, S and Singer, LN and Gauthier, L and Gagnon, MP and Gagnon, MA and Martorella, G},
title = {Educational needs and preferences of adult patients with acute pain: a mixed-methods systematic review.},
journal = {Pain},
volume = {165},
number = {12},
pages = {e162-83},
pmid = {38888742},
issn = {1872-6623},
abstract = {Many patients experience acute pain, which has been associated with numerous negative consequences. Pain education has been proposed as a strategy to improve acute pain management. However, studies report limited effects with educational interventions for acute pain in adults, which can be explained by the underuse of the person-centered approach. Thus, we aimed to systematically review and synthetize current evidence from quantitative, qualitative and mixed-methods studies describing patients' needs and preferences for acute pain education in adults. We searched original studies and gray literature in 7 databases, from January 1990 to October 2023. Methodological quality was assessed with the Mixed Methods Appraisal Tool. A total of 32 studies were included (n = 1847 patients), two-thirds of which were qualitative studies of high methodological quality. Most of the studies were conducted over the last 15 years in patients with postsurgical and posttraumatic pain, identified as White, with a low level of education. Patients expressed the greatest need for education when it came to what to expect in pain intensity and duration, as well how to take the medication and its associated adverse effects. The most frequently reported educational preferences were for in-person education while involving caregivers and to obtain information first from physicians, then by other professionals. This review has highlighted the needs and preferences to be considered in pain education interventions, which should be embedded in an approach cultivating communication and partnership with patients and their caregivers. The results still need to be confirmed with different patient populations.},
}

@article {pmid39542998,
year = {2024},
author = {Kober, SE and Wood, G and Berger, LM},
title = {Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {39542998},
issn = {1573-3270},
abstract = {The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.},
}

@article {pmid39539351,
year = {2024},
author = {Liu, M and Fang, M and Liu, M and Jin, S and Liu, B and Wu, L and Li, Z},
title = {Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1486167},
doi = {10.3389/fnhum.2024.1486167},
pmid = {39539351},
issn = {1662-5161},
abstract = {BACKGROUND: Although the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.

METHODS: The study retrieved relevant publications on BCI technology in rehabilitation from the Web of Science Core Collection (WoSCC) between January 1, 2004, and June 30, 2024. The search was conducted using thematic queries, and the document types included "original articles" and "review articles." Bibliometric analysis and knowledge mapping were performed using the Bibliometrix package in R software and CiteSpace software.

RESULTS: During the study period, a total of 1,431 publications on BCI technology in rehabilitation were published by 4,932 authors from 1,281 institutions across 79 countries in 386 academic journals. The volume of research literature in this field has shown a steady upward trend. The United States of America (USA) and China are the primary contributors, with Eberhard Karls University of Tübingen being the most active research institution. The journal Frontiers in Neuroscience published the most articles, while the Journal of Neural Engineering was the most cited. Niels Birbaumer not only authored the most articles but also received the highest number of citations. The main research areas include neurology, sports medicine, and ophthalmology. The diverse applications of BCI technology in stroke and spinal cord injury rehabilitation, as well as the evaluation of BCI performance, are current research hotspots. Moreover, deep learning has demonstrated significant potential in BCI technology rehabilitation applications.

CONCLUSION: This bibliometric study provides an overview of the research landscape and developmental trends of BCI technology in rehabilitation, offering valuable reference points for researchers in formulating future research strategies.},
}

@article {pmid39539350,
year = {2024},
author = {Xia, R and Yang, S},
title = {Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1423382},
doi = {10.3389/fnhum.2024.1423382},
pmid = {39539350},
issn = {1662-5161},
abstract = {This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents (N = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.},
}

@article {pmid39537730,
year = {2024},
author = {Tian, P and Xu, G and Han, C and Du, C and Li, H and Chen, R and Xie, J and Wang, J and Jiang, H and Guo, X and Zhang, S and Wu, Q},
title = {A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {27872},
pmid = {39537730},
issn = {2045-2322},
support = {No.20231055//The First Affiliated Hospital of Xi'an Jiaotong University/ ; No.20231056//The First Affiliated Hospital of Xi'an Jiaotong University/ ; Program No. 2023-JC-QN-0501//Natural Science Basic Research Program of Shaanxi Province/ ; No.2021ZD0204300//National Key Research and Development projects/ ; },
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Software ; Adult ; Algorithms ; Female ; Young Adult ; Fatigue/physiopathology ; Asthenopia/physiopathology ; Photic Stimulation ; },
abstract = {With the development of brain-computer interface industry, large amounts of related applications have entered people's vision. BCI applications based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and have high information transmission rates. However, in the actual use of SSVEP stimulus paradigm, the subjects will produce visual fatigue with the use, and fatigue will affect the transmission efficiency. In this experiment, an experimental environment consisting of two paradigm stimulus frequencies (7.5 Hz, 15 Hz), three resolutions (800 × 600, 1280 × 720, 1920 × 1080) and three refresh rates (120 Hz, 240 Hz, 360 Hz) is set up. The Likert scale is used to collect subjective fatigue and preference scores, and the EEG acquisition system and eye tracker are used to collect objective data. Using the proposed information entropy-CRITIC algorithm to combine subjective and objective indicators, a fatigue assessment system (display screen fitness-DSF) is innovated to score different experimental environments. The higher the DSF score, the better the visual experience. The results show that when using the 7.5 Hz SSVEP paradigm, the combination of 360 Hz and 1920 × 1080 can bring the best visual experience. When using the 15 Hz SSVEP paradigm, the combination of 240 Hz and 1280 × 720 is the best. DSF provides powerful help for hardware and software selection guidance and vision protection when using SSVEP-based BCI applications.},
}

Humans
*Evoked Potentials, Visual/physiology
*Brain-Computer Interfaces
*Electroencephalography/methods
Male
*Software
Adult
Algorithms
Female
Young Adult
Fatigue/physiopathology
Asthenopia/physiopathology
Photic Stimulation

@article {pmid39537459,
year = {2024},
author = {Lai, W and Sha, L and Li, R and Yu, S and Jin, L and Yang, R and Yang, C and Chen, L},
title = {Urine multi-omics markers to predict seizure one day in advance.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2024.10.035},
pmid = {39537459},
issn = {2095-9281},
}