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• Application of Motor Imagery Brain-Computer Interface Training Based on Electroencephalogram in Stroke Rehabilitation
  crossref Yulong BAI 2026-01-03T03:09:57Z 置信度 0.70
• “MindDrive” - Personalized Model Using EEG-Based Brain-Computer Interface for Wheelchair Control
  crossref Aanya Krishna 2026-08-04T12:02:29Z 置信度 0.70
• From Micro-Signals to Macro-Trends: A Hierarchical Deep Learning Framework for Forecasting Brain-Computer Interface Innovations
  Forecasting emerging technological opportunities in early-stage innovations, such as brain-machine interface(BCI), is challenging due to the inherent stochasticity and extreme noise of individual patent data. While existing trend analyses frequently rely on na…
  crossref Xinyu zhang, Shanshan Pu 2026-06-06T09:29:36Z 置信度 0.70
• Application Without Machine Learning
  crossref Ildar Rakhmatulin, Ganesh R. Naik 2026-04-12T22:28:36Z 置信度 0.70
• Case Studies and Applications
  crossref Ildar Rakhmatulin, Ganesh R. Naik 2026-04-12T22:18:23Z 置信度 0.70
• Machine Learning: EEG Perspective
  crossref Ildar Rakhmatulin, Ganesh R. Naik 2026-04-12T22:18:36Z 置信度 0.70
• Dataset for Machine Learning
  crossref Ildar Rakhmatulin, Ganesh R. Naik 2026-04-12T22:41:36Z 置信度 0.70
• Speech Decoding from Non-invasive Brain Signals Using Generative Learning Framework
  crossref Seo-Hyun Lee, Soowon Kim, Jun-Young Kim, Seong-Whan Lee 2026-03-19T20:04:24Z 置信度 0.70
• Impact of amplitude errors and uncertainties in the measurement of brain potentials through wearable EEG devices
  This work investigates the amplitude of signals measured by electroencephalographic devices. Indeed, acquisition latencies may affect the detection of time-locked neural phenomena, especially in wireless equipment. Nonetheless, further sources of errors and un…
  crossref Pasquale Arpaia, Antonio Esposito, Enza Galasso, Fortuna Galdieri 2026-03-19T20:04:24Z 置信度 0.70
• The Future of Communication Skills Training in Neuro-Digital Workplaces
  In the age of Digital Transformation and 'neuroscience' what digital transformation does to workplaces is a paradigm change that we now call the Neuro-digital Workplace. Not only will these spaces be defined by state-of-the-art technology, but the incorporatio…
  crossref Vartika Chaturvedi, Anjali Khanna 2026-07-24T15:37:15Z 置信度 0.70
• Neurotechnology, Mental Health, and Gender-Neutral Justice
  In the field of mental health, public policy and a gender-neutral justice-for-all are where Brain-Computer Interfaces (BCIs) come into play as emerging neurotechnological means. This chapter discusses the potential of BCIs to mitigate experience of cruelty and…
  crossref Yash Saxena, Hemant Singh, Radha Naruka 2026-07-24T15:37:15Z 置信度 0.70
• Towards Neuroadaptive Technology: Ongoing reflections on Project NAFAS
  crossref Laurens R. Krol, Matthias Pesch, Thorsten O. Zander 2026-03-19T20:04:24Z 置信度 0.70
• Brain-Computer Interface Based Mental Workload Classification: Utilizing EEG Signal and N-Back Test Experiment
  Mental workload (MWL) plays an important role in human performance, especially in high-risk settings like aviation and emergency services. Overload and underload of cognitive resources can impair decision-making, result in slow response time, and undermine tas…
  crossref Piyush Meshram, Mitul Kumar Ahirwal 2026-03-18T19:37:12Z 置信度 0.70
• One Signal, Many Functions: How Overlapping Neural Processes Complicate BCI-based Neurofeedback
  crossref Maria Pfeiffer, Andrea Kübler 2026-03-19T20:04:24Z 置信度 0.70
• Inter-Session Variability in Long-Term ECoG BCIs
  Decoder performance in brain–computer interfaces (BCIs) can vary substantially across sessions, partly due to progressive changes in the distribution of neural features. Despite its impact on decoding stability and clinical usability, inter-session variability…
  crossref Maria Justino, Felix Martel, Tetiana Aksenova 2026-03-19T20:04:24Z 置信度 0.70
• Analytical Study of Key Techniques for Cross-Modal Feature Alignment and Decision-Level Fusion in Brain–Computer Interface-Virtual Reality Systems
  Feature alignment and decision-level fusion in multimodal BCI–VR interaction were investigated using Transformer-based cross-modal embeddings, Lab Streaming Layer time synchronization, attention masks, and wavelet filtering for robust representation. A four-mo…
  crossref Dan Liu 2026-06-29T08:21:28Z 置信度 0.70
• When Chinese verb transitivity meets wrong syntactic category.
  Two experiments using the same stimuli were performed to examine whether verb transitivity processing proceeds even when phrase structure building based on syntactic categories (noun, verb, etc.) fails during Chinese sentence reading. The sentences contained (…
  pubmed Zhang Y, Piao Q, Yang Y, Boland JE 等 2026 置信度 0.82
• Correction to: Life Cycle and Circadian Rhythms in Central Resident Immunity and Neuropsychiatric Pathology.
  europepmc 2026 置信度 0.80
• PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.
  Cell type annotation in spatial transcriptomics (ST) is fundamental for deciphering complex tissue organization and spatially resolved biological processes. Most existing methods perform ST cell type annotation by transferring labels from single-cell RNA-seq (…
  pubmed Xu Y, Wang X, Liu S, Ge C 等 2026 置信度 0.82
• Synergistic EEG signal processing for brain-computer interfaces using hybrid MothCray optimization and deep learning.
  Electroencephalography (EEG) based brain-computer interface (BCI) systems require optimal channel selection to achieve high signal quality, reduced setup complexity, and robust usability. This study introduces a comprehensive signal-processing framework to imp…
  pubmed Kumari A, Edla DR, Ramesh D, Shirodkar VR 2026 置信度 0.82
• Molecular Mechanisms of Foreign Body Responses to Neural Electrodes and Surface Biofunctionalization Strategies for Interface Modulation.
  Long-term implantable neural electrodes underpin brain-machine interfaces, deep brain stimulation, epilepsy monitoring, and closed-loop neuromodulation. Following chronic implantation, however, the foreign body response (FBR) at the electrode-tissue interface …
  pubmed He Z, Ma J, Liu Y, Du Z 2026 置信度 0.82
• NeuroLens: A Holistic Visual Analytics System for Exploring Brain Networks Across Scales.
  Identifying biomarkers from human brain networks is critical in early detection of neurological disorders and understanding disease mechanisms. Existing visual analytics approaches show a remarkable ability to assist experts in discovering and validating bioma…
  europepmc 2026 置信度 0.80
• Advancing Nursing Cognitive Capacity Through Generative AI and Immersive VR as a Structural Intervention for Burnout and Administrative Burden.
  Nurses practice in clinical environments shaped by escalating administrative demands, documentation burdens, and persistent cognitive overload, structural pressures that contribute significantly to burnout and workforce attrition. Generative artificial intelli…
  pubmed Nnaka T, McMahan T, Teplitskiy N, Arcuri S 等 2026 置信度 0.82
• Acoustic separation of cells and bacteria in open sessile droplets.
  Efficient and non-destructive separation of cells and pathogenic microorganisms is a core technical bottleneck in clinical point-of-care diagnostics. Traditional separation methods mostly rely on microchannel structures, external fluid actuation, or biochemica…
  pubmed Liu X, Li X, Quan H, Yu Z 等 2026 置信度 0.82
• Diffusion Inverse Filtering: Enhancing Functional Connectivity-Based Pattern Recognition by Counteracting Spatial Smoothing.
  Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their discrimi…
  pubmed Xu Y, Otsuka S, Nakagawa S 2026 置信度 0.82
• Neurotechnological Natives and the Neurotechnology Shift: A Research Agenda for the Brain-as-Interface Era.
  Neurotechnologies are increasingly moving beyond clinical and laboratory environments into a growing range of everyday contexts, including consumer markets, workplaces, and educational settings. This article presents the concept of the neurotechnology shift, u…
  pubmed Muñoz JM 2026 置信度 0.82
• Application of non-invasive electroencephalogram-based brain-computer interfaces in post-stroke hand function rehabilitation.
  Post-stroke hand dysfunction severely limits patients' independence, and conventional rehabilitation often fails those without active movement. Noninvasive EEG-based brain-computer interface (BCI) technology addresses this by creating a closed-loop feedback sy…
  pubmed Peng W, Yang Y, Wang J, Ren X 等 2026 置信度 0.82
• Flexible bioelectronic electrodes: From engineering strategies to cross-domain biomedical applications.
  Flexible electrodes have emerged as key bioelectronic interfaces for recording, modulation, and sensing in complex biological environments. Their mechanical compliance, conformal contact, and tunable electrochemical properties enable effective coupling between…
  pubmed Zuo X, Sun X, Cong L, Wang H 等 2026 置信度 0.82
• Cross-task, explainable and real-time decoding of human emotion states by integrating gray and white matter intracranial neural activity.
  Decoding human emotion states from intracranial neural activity is key in developing affective brain-computer interfaces and new therapies for affective disorders. However, real-world application of decoding requires high performance that integrates neural act…
  pubmed Yang Y, Chen W, Chen Y, Ding L 等 2026 置信度 0.82
• Mapping the Therapeutic Network of Emotion Recovery Across Psychiatric Disorders.
  pubmed Liao W 2026 置信度 0.82
• Dual-mode thermo-responsive microneedle liposome patch for adaptive transdermal delivery in postherpetic neuralgia.
  Postherpetic neuralgia (PHN) presents as persistent background pain accompanied by unpredictable breakthrough episodes. Current topical therapies are poorly suited to this fluctuating pain pattern because they provide limited transdermal penetration and static…
  pubmed Xiao X, Sheng X, Li S, Chen X 等 2026 置信度 0.82
• Adoption of Deep Learning Methods for SSVEP Classification in XR-Based Wearable Brain-Computer Interfaces.
  This paper addresses steady-state visually evoked potential (SSVEP) classification in wearable extended-reality (XR) brain-computer interfaces (BCIs), with a threefold objective. First, it investigates the effectiveness of deep learning (DL)-based SSVEP classi…
  pubmed Angrisani L, De Benedetto E, De Maria A, Duraccio L 等 2026 置信度 0.82
• A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).
  Motor imagery (MI) classification serves as the backbone to brain-computer interfaces (BCIs) by strengthening the communication bridge between the human brain and external peripheral devices. The past two decades have witnessed unprecedented success in MI-BCIs…
  pubmed Abbasi MA, Abbasi HF, Arsalan M, Khan D 等 2026 置信度 0.82
• Identifying the seizure onset zone with phase-amplitude coupling.
  Accurate identification of the seizure onset zone (SOZ) is critical for the diagnosis and treatment of drug-resistant epilepsy (DRE). In recent years, although phase-amplitude coupling (PAC) has played an important role in epilepsy-related studies, few investi…
  europepmc 2026 置信度 0.80
• Shared and culture-specific brain networks for emotional facial discrimination: Evidence from predictive modeling.
  Whether emotional facial expressions are perceived universally or are culture-specific has been a topic of debate in affective neuroscience. To address this, we employed connectome-based predictive modeling (CPM) to analyse whole-brain connectivity data from a…
  pubmed Amos TJ, Han W, Sun R, Jin Z 等 2026 置信度 0.82
• Brain-spine interface: an exploration of a potential future treatment for spinal cord injury.
  Spinal cord injuries (SCIs) profoundly impact millions globally, leading to loss of motor and sensory functions below the injury site. Brain-spine interfaces (BSIs) represent an early-stage neuroprosthetic strategy that attempts to restore functional communica…
  pubmed Sivan V, Alam Z, Polavarapu H, Manivel S 等 2026 置信度 0.82
• Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.
  The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic regulation, adaptive connectivity, and oscillatory dynamics. By contrast, electroencephalographic (EEG) recordi…
  pubmed Dang T 2026 置信度 0.82
• Comparative Analysis of Tri-Polar Concentric Ring and Conventional Electrodes for Overt and Covert Speech.
  The Brain-Computer Interface (BCI) is a system that enables communication between the brain and external devices by translating brain activity into commands. Electroencephalography (EEG) is a commonly used modality for measuring brain activity. However, its lo…
  pubmed Memon PQ, Anderson C, Memon ZQ, Memon S 等 2026 置信度 0.82
• The role of inferior frontal gyrus in emotion regulation: Evidence from fMRI and tDCS investigation.
  Previous neuroimaging studies indicate that the inferior frontal gyrus (IFG) may be associated with emotion regulation through the use of cognitive reappraisal strategy. However, whether there is a causal role of IFG in reappraisal-based regulation of general …
  pubmed Li W, Ngetich RK, Zhang Q, Zhang J 等 2026 置信度 0.82
• HSPA8 orchestrates SNARE complex assembly to drive extracellular vesicle-mediated spread of p-tau217 in Alzheimer's disease.
  Dysregulation of multivesicular bodies (MVBs) in Alzheimer's disease (AD) contributes to aberrant tau secretion via extracellular vesicles (EVs). This may potentially explain our previous paradoxical observation of elevated free-form p-tau217 alongside reduced…
  pubmed Xu B, Guo Z, Chen J, Xie Y 等 2026 置信度 0.82
• VlPAG/DRN Microglia Drive Neuropathic Pain-Induced Depression via a Defined Neuroimmune Axis.
  Neuropathic pain is frequently comorbid with anxiety and depression, yet the mechanisms linking immune signaling to affective brain circuits remain poorly understood. Here, we identify a neuroimmune circuit in which peripheral nerve injury activates microglia …
  pubmed Yang J, Tang BJ, Li JY, Xie SL 等 2026 置信度 0.82
• Exploring Kinematics Information Decoding from EEG Slow Cortical Potentials During Movement Imagination and Observation.
  Motor Imagery-based brain computer interface (MI-BCI) systems capable of decoding imagined movements and their kinematics are a rapidly advancing area of BCI research. Such BCIs can enhance human-computer interaction and have potential neurorehabilitation and …
  pubmed Kutteri SG, Vinod AP 2026 置信度 0.82
• [Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies].
  Brain-computer interfaces (BCIs) has developed rapidly in recent years, yet a systematic understanding of its inherent limitations and capability boundaries remains insufficient. This paper analyzes the constraint mechanisms through which inherent limitations …
  pubmed Fu Y, Yao H, Li T, Zhao L 等 2026 置信度 0.82
• The MPOA-VTA Pathway: From Pup Care to Adult Helping.
  pubmed Chen K, Fan J, Xu H 2026 置信度 0.82
• A Deep Learning Framework for EEG-Based Decoding of Visually Imagined Arrows with Different Colors and Directions.
  Brain-computer interface (BCI) systems have demonstrated significant potential across medical, educational, and entertainment domains. Recently, visual imagery (VI) has emerged as an alternative to traditional motor imagery (MI) paradigms, offering a broader s…
  pubmed Alazrai R, Hatahet O, Qaadan S, Alothman Y 等 2026 置信度 0.82
• Clinical translation and accessibility of brain-computer interfaces: From technology development to clinical application.
  Brain-computer interface (BCI) technology establishes a direct communication pathway between neural activity and external devices. Driven by advances in neuroscience, artificial intelligence (AI), neural signal acquisition, decoding algorithms, and implantable…
  pubmed Sun H, Wang R, Karako K, Song P 等 2026 置信度 0.82
• A Novel Rat Robot: Multi Degree of Freedom Motion Control.
  Objective The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode f…
  europepmc 2026 置信度 0.80
• Deep Transfer Learning in Intra-Subject and Inter-Subjects for Intracortical Brain Machine Interface Decoding.
  Objective This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain-machine interface (iBMI) systems while reducing the reliance on new labeled samples. Methods ID…
  europepmc 2026 置信度 0.80
• DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic Control.
  Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain-co…
  pubmed Zhang Y, Shao Q, Yang H, Ren X 等 2026 置信度 0.82
• EEG-based hypoglycemia detection in Type 1 Diabetes: Proof-of-concept study.
  Early detection of hypoglycemia among non-hypoglycemic conditions is critical in type 1 diabetes (T1D), as delayed intervention can lead to serious neurological and metabolic consequences. Although continuous glucose monitoring (CGM) systems provide continuous…
  pubmed Kubaščík M, Aggarwal S, Karpiš O, Tupý A 等 2026 置信度 0.82
• Deep learning segmentation with curvature consistency reveals astrocyte nanostructure across species.
  Cellular function depends on the precise deployment and distribution of nanoscale structures, but these features remain difficult to measure and compare between cells and datasets. This challenge is pronounced for astrocytes, whose intricate nanostructures int…
  pubmed Fok AHK, Wang Y, Ng M, Syed T 等 2026 置信度 0.82
• EFS-NET: EEG-fNIRS multi-scale fusion network based on spatial calibration.
  Hybrid brain-computer interfaces (hBCIs) integrate multiple neuroimaging modalities and utilize their complementary information to address the inherent limitations of single-modality neural signal decoding. For electroencephalography (EEG) and functional near-…
  pubmed Gu W, Daly I, He X, Li S 等 2026 置信度 0.82
• A Unified Framework of Functional Clinical Outcomes for Implantable Motor Brain-Computer Interfaces: From Digital Motor Outputs to Critical Digital Activities.
  Implantable brain-computer interfaces (iBCIs) are at a pivotal inflection point. Multiple commercial ventures are advancing toward regulatory approval, and the challenge facing the field is transitioning from demonstrating safety and feasibility to achieving r…
  pubmed A S, I B, M W, N R 等 2026 置信度 0.82
• An Augmented Reality and AI-Based System for Contextual Appliance Guidance: Implications for Cognitive Accessibility and Assistive Interaction.
  Modern household appliances often present complex interfaces that can be difficult to use, especially for older adults, people with visual impairments, and users with mild cognitive difficulties. In such cases, interacting with appliances may require sustained…
  pubmed Hafezi K, Tafreshi AH, Napoli C, Randieri C 等 2026 置信度 0.82
• Enhancing Motor Imagery Decoding for Stroke Patients Using Data Augmentation and Transfer Learning.
  This study proposes an innovative WalkBCI real-time motor imagery brain-computer interface system to solve the problems of time-consuming model calibration and low EEG decoding accuracy in stroke patients because of individual differences. WalkBCI integrates g…
  pubmed Xu F, Sun Y, Lun Z, Zhao P 等 2026 置信度 0.82
• ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification.
  Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We evaluated whether prototype-based cross-attention can provide competitive, inherently interpretable event-related potential (ERP…
  pubmed Wyman CG, Hirshfield L 2026 置信度 0.82
• EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network With Channel Selection.
  Electroencephalogram (EEG)-based emotion recognition holds great potential in intelligent human-computer interaction and brain-computer interface systems, as the brain generates distinct electrical activity patterns under different emotional states. However, E…
  europepmc 2026 置信度 0.80
• Transfer learning for target user in motor imagery EEG recognition.
  Target user EEG identification for two-class motor imagery is crucial In the field of brain computer interface (BCI). We propose a lightweight method combining transfer learning (TL) and wavelet packet transform (WPT). After common average reference preprocess…
  pubmed Cai M, Qu Y 2026 置信度 0.82
• Eye-Movement-Assisted Time-Frequency EEG Decoding for Multimodal Robotic Arm Control.
  Brain-computer interface (BCI) technology has shown potential for future rehabilitation-related and assistive control applications. Nevertheless, single-modality electroencephalography-based motor imagery (EEG-MI) signals are susceptible to interference, where…
  pubmed Sun X, Zhang W, Wu J, Xiong X 等 2026 置信度 0.82
• Graph convolutional network-based harmonization of EEG for cross-dataset transfer in motor imagery in BCI.
  Objective . Electroencephalogram (EEG) electrode configurations vary across Motor Imagery Brain-Computer Interface (MI-BCI) datasets, limiting transfer learning and system performance due to small dataset sizes. This work proposes a spatial harmonization frame…
  pubmed K M D, Parashiva PK, Vinod AP 2026 置信度 0.82
• Cortical-SSM: a deep state space model for motor imagery decoding from EEG signals.
  Objective. Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments. These signals remai…
  pubmed Suzuki S, Nagashima S, Sugiura K 2026 置信度 0.82
• Brain-computer interfaces: A lifeline for paralysis or a Pandora's box for humanity?
  Recent advances in computerized technologies, neuroscience, and materials and engineering have transformed brain‑computer interfaces (BCIs) from conventional unidirectional signal recording systems (brain-to-device) to bidirectional closed-loop neuromod…
  pubmed Asakawa T 2026 置信度 0.82
• BiGSTF-Net: inter-modal mutual guidance and intra-modal spatio-temporal fusion for EEG-fNIRS cognitive classification.
  Objective. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide complementary temporal and spatial information for brain-computer interfaces (BCIs). However, effectively exploiting this complementarity remains challenging due …
  pubmed Tao S, Feng L, Jia S, Xu B 等 2026 置信度 0.82
• Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources.
  Computational technology opens new possibilities toward understanding the complexity of the human brain, but it requires integrating measurements from different modalities and scales in an anatomical context and exposing them in an interoperable, actionable fo…
  pubmed Dickscheid T, Gui X, Simsek AN, Schiffer C 等 2026 置信度 0.82
• Active devices and systems for closed-loop neuromodulation.
  Neuromodulation has become a central topic in neuroscience and biomedical engineering, as it provides powerful means to interrogate and regulate neural activity and offers promising therapeutic strategies for a wide range of neurological disorders. Conventiona…
  pubmed Wang J, Xue H, Li J, Li Y 等 2026 置信度 0.82
• Brain-CLIPLM: semantic compression for EEG-to-text decoding.
  Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central question: can sentence-level language be reliably recovered from such signals? Un…
  pubmed Yang X, Tian H, Li Y, Zhang J 等 2026 置信度 0.82
• Integrated decoding of local and prospective spatial representations for future decision prediction.
  Objective. Decoding future spatial decisions from the brain's cognitive map represents a critical step toward cognitive brain-computer interfaces (BCIs) and closed-loop neuromodulation for neurological disorders. However, accurately predicting future paths fro…
  pubmed Wang X, Luo X, Wang Y, Deng X 等 2026 置信度 0.82
• Injectable Antifouling Adhesive Hydrogel Enables Robust Neural Interfaces for Stable ECoG Recording.
  Micro-electrocorticography (micro-ECoG) provides high-spatiotemporal-resolution cortical sensing for brain-computer interfaces, but stable subdural recording remains difficult because dural opening disrupts barrier integrity, cortical micromotion weakens devic…
  pubmed Peng J, Li X, Li W, Gao C 等 2026 置信度 0.82
• Mental bootstrapping enables human-level concept learning in self-supervised deep models.
  How agents acquire abstract concepts from sparse, diverse examples-often without explicit supervision-remains a central problem in cognitive science and artificial intelligence. Human studies suggest that this ability depends on mental bootstrapping, the gradu…
  pubmed Yang L, Lyu M, Wang YJ, Xie X 等 2026 置信度 0.82
• Effectiveness of Neurological Physiotherapy Interventions in Post-stroke Motor Rehabilitation: A Systematic Review.
  Stroke frequently results in persistent upper-limb, lower-limb, and gait impairments that restrict independence and quality of life. Neurological physiotherapy aims to enhance post-stroke motor recovery through task-specific practice, strengthening, sensory-mo…
  pubmed Ankireddypalli S, Avadhani D, Roy MS, Kumar Y 等 2026 置信度 0.82
• Evaluating multi-level membership inference risk in federated EEG learning.
  Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordin…
  pubmed Khanam T, Siuly S, Wang K, Whittaker F 等 2026 置信度 0.82
• Editorial: Advances in perceptual learning: new directions, techniques, and applications.
  pubmed He Q, Huang CB, Chen J, Thompson B 2026 置信度 0.82
• Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture.
  Brain-computer interface (BCI) systems have advanced with deep learning, but they are still limited by designs tied to specific applications, poor scalability, weak portability, the need for user-specific adaptation, and privacy concerns. We present BELT, a mo…
  pubmed Danayi A, Soltanian-Zadeh H 2026 置信度 0.82
• Multimodal Assessment of Consciousness with Brain-Computer Interfaces and Artificial Intelligence: From Acquired Brain Injury to Neurodegenerative Disease.
  The assessment of consciousness has been shaped largely by research on acquired disorders of consciousness after acute or chronic brain injury, but similar problems of unreliable behavioral expression increasingly arise in neurodegenerative disease. This trans…
  pubmed Kordas B 2026 置信度 0.82
• Enhance motor imagery EEG classification using DWT and chirplet transform.
  Brain Computer Interfaces promote seamless interaction between individuals with movement limitations and their surrounding environment by transforming electroencephalography signals derived using Motor Imagery. The procedure relies on accurately classifying va…
  pubmed Patil S, Sultane D, Shah PK 2026 置信度 0.82
• Cognitive load gating system in motor imagery BCIs: a dual-task EEG study with differential entropy-based reliability estimation.
  Brain-computer interface (BCI) systems based on motor imagery hold significant clinical value for individuals who have lost voluntary movement, but most studies test BCI assistive devices like powered wheelchairs under ideal conditions where motor imagery sign…
  pubmed Haridharan H, Dhanasekar G, Nageswaran S 2026 置信度 0.82
• Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.
  Memory prosthetics, closed-loop brain-computer interfaces that decode hippocampal activity and deliver adaptive stimulation, are transitioning from animal proof-of-concept to first-in-human trials. Realizing chronically implantable systems requires co-design o…
  pubmed Mo F, Zhao X, Xu Y, Tang C 等 2026 置信度 0.82
• Enhancing Brain Signal Generation Through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models.
  Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and inter-individual variability. To address these limitatio…
  europepmc 2026 置信度 0.80
• Bimanual attempts improving EEG-based hand movement decoding in stroke patients with hemiplegia.
  Brain-computer interfaces (BCIs) based on electroencephalography (EEG) signals have been applied to improve active hand motor function rehabilitation of stroke patients. Besides, the bilateral arm rehabilitation training can effectively promote the recovery of…
  pubmed Wang J, Bi L, Fei W, Wei Y 2026 置信度 0.82
• The timing of visual selective attention in fronto-parietal network: TMS behavioral and brain structural evidence.
  Neuronal activation within the fronto-parietal network (FPN) exhibits distinct time windows during bottom-up and top-down attention. Previous studies have shown that transcranial magnetic stimulation (TMS) applied to the FPN can have inhibitory effects on atte…
  pubmed Zhang Q, Zhang D, Alimu G, Guragai B 等 2026 置信度 0.82
• The emerging field of cognitive brain-computer interfaces.
  Brain-computer interfaces (BCIs) have achieved transformative success in restoring movement and communication. However, extending these approaches to decoding or recovery of cognitive function, such as attention or memory, poses fundamentally new challenges. C…
  pubmed Saez I 2026 置信度 0.82
• Thrivaad: A Multilingual, Predictive Eye-Sign-Based AAC System Powered by Optimized Deep Learning.
  Around 1.5% of the global population is suffering from speech impairments; the major causes for this are cerebral palsy and ALS, and the only way for these individuals to communicate is through Augmentative and Alternative Communication (AAC). These systems ar…
  pubmed Megalingam RK, Kuttankulangara Manoharan S, Cheriyan Manjooran D, Kamble D 2026 置信度 0.82
• Graph convolution neural network channel selection with attention for motor imagery EEG decoding.
  Accurate decoding of motor imagery electroencephalography (MI-EEG) signals is critical for practical brain-computer interface (BCI) systems. However, conventional approaches typically rely on dense multi-channel recordings, which not only introduce data redund…
  pubmed Li H, Dang W, Liu L, Du P 等 2026 置信度 0.82
• A novel deep learning approach for privacy-preserving encoded EEG-based brain-computer interfaces with clinical LLM applications.
  The rise of large language models (LLMs) such as GPT-4 and DeepSeek has transformed healthcare information processing by enabling natural language-based clinical reasoning. However, the integration of LLMs with privacy-sensitive biomedical signals, particularl…
  pubmed Khanam T, Siuly S, Wang K, Wang H 2026 置信度 0.82
• Seeing Avatars During Social Interaction in VR May Enhance Inter-Brain Synchronization.
  Does seeing avatars during social interaction in virtual reality (VR) affect the neural synchrony of interacting people? To address this question, we assessed inter-brain synchronization (IBS), which reflects synchronization of brain activities among interacti…
  europepmc 2026 置信度 0.80
• Male-Biased Social Deficits in Chd8<sup>+/R2219*</sup> Mouse Model of Autism Linked to Hippocampal Abnormalities.
  Autism spectrum disorder (ASD) is a neurodevelopmental condition with a 4:1 male bias, yet the biological basis for this sex difference remains poorly understood. Heterozygous mutations in the chromatin remodeler CHD8 confer high risk for ASD, with phenotypes …
  pubmed Zhang Q, Qiao Y, Li H, Zeng J 等 2026 置信度 0.82
• A Dual-Branch Spatiotemporal Framework with Dynamic Weighted Permutation Entropy for Short-Window Motor Imagery EEG Decoding.
  Decoding short-window electroencephalography (EEG) signals is critical for low-latency brain-computer interfaces (BCIs), yet current models struggle to extract robust features under high cross-subject variability and low signal-to-noise ratios. To address this…
  pubmed Wang J, Yang H 2026 置信度 0.82
• Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning.
  A brain-computer interface (BCI) is an advanced technology that enables direct communication between the human brain and external systems, eliminating the need for intermediaries. Electroencephalography (EEG) is a commonly used signal for developing BCIs. Howe…
  europepmc 2026 置信度 0.80
• Successful Single-Session Neural Self-Regulation Through Neurofeedback Varies Between Features.
  Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn the neural self-regulation required to control the feedback. Un…
  pubmed E S, J S, E A 2026 置信度 0.82
• Contrastive Learning Network based on Multi-Scale Transformer (CLMT-net): EEG decoding for fine-grained motor imagery of movements within the same limb.
  Motor imagery (MI)-based brain-computer interfaces (BCIs) decode EEG signals into control commands. However, fine-grained MI decoding within the same limb remains challenging due to highly similar neural patterns. This paper proposes a Contrastive Learning Net…
  pubmed Zhu L, Yue Q, Huang A, Zhang J 等 2026 置信度 0.82
• Joint-Shrinkage Pattern Matching for Small-Sample and Imbalanced ERP Decoding in Brain-Computer Interfaces.
  Event-related potential (ERP)-based brain-computer interface (BCI) systems are approaching sub-microvolt-level resolution, enabling detailed decoding of sophisticated cognitive processes. This progress has increased the demand for robust classifiers. Current a…
  europepmc 2026 置信度 0.80
• Real-time emotion recognition based on EEG signals using a hybrid batch-stream architecture.
  In recent years, emotion recognition using brain-computer interface (BCI) systems has gained substantial attention. Existing models are typically implemented in either offline (batch) or online (streaming) modes. While batch processing approaches generally ach…
  europepmc 2026 置信度 0.80
• Hands-free motor imagery EEG classification via LLM multi-agents.
  Motor imagery (MI) brain-computer interfaces (BCI) rely on precise electroencephalogram (EEG) classification. However, issues such as the reliance on extensive manual experience for MI-EEG model design, parameter tuning, and optimization directions, along with…
  pubmed Zhao R, Li S, He X, Wang X 等 2026 置信度 0.82
• Deep-learning based electroencephalogram denoising: a literature review.
  Electroencephalography (EEG) is a pivotal tool for exploring brain functions. However, the low amplitude of EEG signals renders them inherently susceptible to contamination from diverse physiological and environmental artifacts, including electromyogram artifa…
  pubmed Wu L, Liu A, Wang ZJ, Ward RK 等 2026 置信度 0.82
• MI recognition by subject specific localised frequency fusion.
  EEG-based motor imagery (MI) discrimination is widely utilised in real-life applications, such as brain-computer interfaces (BCIs), due to its non-invasive and comparatively cost-effective nature. However, traditional BCI systems typically rely on a uniform, b…
  pubmed Rahman MKM, Shuvo HMT 2026 置信度 0.82
• Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.
  Individual detachment-reintegration processes offer a useful window into brief non-steady reorganization in collective flight, but whether such events can be identified objectively across flight trials remains unclear. Using high-resolution three-dimensional t…
  pubmed Huang Y, Li M, Shang Z, Yang L 2026 置信度 0.82
• Align and Fuse: A Transformer-Based Framework for EEG-Augmented Visual Recognition.
  Background : Integrating human neural signals with computational vision systems offers a promising route toward more robust visual recognition, yet supporting mixed-granularity recognition, where both coarse- and fine-grained categories must be distinguished w…
  pubmed Zhang C, Ma Y, Li M, Gao X 等 2026 置信度 0.82
• Improved Spontaneous EEG Signal Decoding Efficiency by Function Predefined Convolutional Neural Network.
  A spontaneous electroencephalogram (EEG)-based brain-computer interface (BCI) is an ideal form of brain-computer interaction. The classical decoding methods can achieve classification by using meaningful manual features, but their performance is poor. The neur…
  europepmc 2026 置信度 0.80
• Widespread white matter microstructural abnormalities in treatment‑naïve patients with first‑episode schizophrenia revealed by fixel-based analysis.
  Fixel-based analysis (FBA) is an advanced diffusion imaging method that enables the direct estimation of white matter microstructural properties beyond the limitations of traditional diffusion tensor imaging (DTI). Despite its potential, FBA has been rarely ap…
  pubmed Yang M, Li H, Li M, Wang Q 等 2026 置信度 0.82
• Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia.
  Brain-computer interface (BCI) inefficiency limits clinical utilization of BCIs, as many users struggle to produce consistent machine-recognizable electroencephalography (EEG) patterns for reliable control. While training can improve BCI performance for most u…
  pubmed Ivanov N, Chau T 2026 置信度 0.82