EEG and Brain-Computer Interfaces

Neueste Paper

• CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

  Jiazhen Hong, Xiaotian Zhou, Zihao Ding, Kailong Wang, Yu Wu · 2. Oktober 2026

  Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer difficult when electrode layouts change. We propose CortexBridge, a lightweight adapter that combines EEG features with electrode and atlas coordinates to…

• From Neurons to Conversation: Speech Brain-Computer Interfaces

  Moein Khajehnejad, Forough Habibollahi, Tommaso Boccato, Margarida Sousa, Michal Olak, Francesco Jamal Sheiban, Matteo Ferrante · 1. Oktober 2026

  Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative intent into external outputs such as text, synthesized voice, or avatar control. Recent advances in intracortical and electrocorticographic recording, de…

• Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness

  Elena Benderskaya, Anastasiia Alifanova, Svetlana Batalova, Vasilisa Zhuk, Anna Kovalenko · 30. September 2026

  A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods of classification, clustering, modeling of brain states under anesthesia and identification of measurable neurobiological characteristics of brain function. A comparative analysis was condu…

• PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG

  Yipeng Zhang, Chenda Duan, Yuanyi Ding, Tianyi Wang, Atsuro Daida, Masaki Izumi, Yuta Tanoue, Naoto Kuroda, Shaun A. Hussain, Nishant Sinha, Eishi Asano. Hiroki Nariai, Vwani Roychowdhury · 30. September 2026

  Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristics, which carry much of the information that downstream tasks depend on. Recent iEEG foundation models learn by reconstructing or predicting their input…

• Spatiotemporal Hyperedges for EEG Seizure Detection and Prediction

  Hyunju Kim, Sheo Yon Jhin, Noseong Park, Nabil Imam · 30. September 2026

  Seizure detection and prediction from EEG are clinically important but challenging because seizures are rare, temporally localized, and propagate as coordinated events across multiple channels. Recent dynamic graph neural networks model this by running a temporal model over a sequence of per-time-st…

• ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding

  Abdul Basit, Saim Rehman, Muhammad Shafique · 29. September 2026

  Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information…

• EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding

  Abdul Basit, Saim Rehman, Muhammad Shafique · 29. September 2026

  Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder can become an overconfident near-one-class predictor. This is especially problematic in source-free deployment, where target-user la…

• AutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain--Computer Interfaces

  Muyun Jiang, Yi Ding, Wei Zhang, Jinbo Chen, Chenyu Liu, Zhenjie Yang, Yuxin Li, Jingyuan Chen, Yuhao Lu, Yong Li, Shuailei Zhang, Cuntai Guan · 29. September 2026

  EEG-based brain-computer interfaces support a broad range of applications, yet designing decoding architectures that perform well across diverse tasks remains challenging. We introduce AutoBCI, an agentic framework in which a Designer Agent and a Forecaster Agent support the discovery and selection …

• Separating Diagnosis from Disease Representation: Dual-View EEG Learning with Neural-Dynamics-Guided Deformation

  Jiaying Wang, Shouqian Shi, Yutong Chen, Xu Yang, Jie Chen, Xingyu Pan, Lei Zhang, Sheng Zhong · 29. September 2026

  Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence wi…

• What your brain activity says about you: A review of neuropsychiatric disorders identified in resting-state and sleep EEG data

  J. E. M. Scanlon, A. Pelzer, M. Gharleghi, K. C. Fuhrmeister, T. K\"ollmer, P. Aichroth, R. G\"oder, C. Hansen, K. I. Wolf · 29. September 2026

  Electroencephalogram monitoring devices and online data repositories hold large amounts of data from individuals participating in research and medical studies without direct reference to personal identifiers. This paper explores what types of personal and health information have been detected and cl…

• AFA-Net: A Differential Attention Approach for Auditory Attention Detection

  Philip H. Lee, Shreeram Suresh Chandra, Karan Thakkar, John H. L. Hansen · 28. September 2026

  Auditory Attention Detection (AAD) utilizes electroencephalographic (EEG) signals to identify a target speaker in a multi-speaker environment. Despite considerable progress, existing deep learning architectures often lack explicit mechanisms for handling noisy EEG data. To address this limitation, w…

• Subject-Invariant Cross-Modal Decoding of Perceived Speech from Brain Recordings

  Aoke Zhang, Jing Chen · 28. September 2026

  Perceived speech decoding based on non-invasive brain-computer interface (BCI) signals has been extensively studied in recent years. Research in this field primarily faces two challenges: extracting neural representations with rich spatiotemporal information and achieving cross-subject generalizatio…

• Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models

  Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu · 28. September 2026

  EEG foundation models increasingly use masked prediction to learn from unlabeled recordings, but optimizing this objective does not ensure transferable neural representations. A central challenge is that stable positional cues and local correlations can make masked regions predictable without integr…

• Personalised federated learning for Riemannian and Euclidean EEG decoding

  Thibault Pautrel, Florent Bouchard, Ammar Mian, Guillaume Ginolhac · 25. September 2026

  Federated learning (FL) lets EEG decoders learn from recordings of several subjects without pooling them. We consider two light EEG decoders, the Riemannian SPDNet and the Euclidean EEGNet. Both split into a trunk, which builds a latent representation, and a head, which classifies it. Inter-subject …

• AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders

  Saim Rehman, Muhammad Shafique · 25. September 2026

  Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI Competition IV-2a, we compare 32-bit floating-point (FP32) EEGNet and ShallowConvNet models with global magnitude pruning…

• Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG

  Frederik M{\o}llskov Trier, Xiaopeng Mao, Sadasivan Puthusserypady · 25. September 2026

  Imagined speech decoding from electroencephalography (EEG) has gained increasing attention as a potential communication pathway for individuals with severe motor impairments, yet reported performance often relies on evaluation protocols that do not clearly reflect cross-subject generalization. This …

• Attention-Enhanced Dual-Branch ConvNeXt-BiLSTM Network for Subject-Independent EEG Seizure Detection

  Maimuna Chowdhury, Sk. Imran Hossain · 22. September 2026

  Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples are substantially outnumbered by non-seizure samples. This paper presents an attention-enhanced dual-branch network that jointly learns time--frequenc…

• Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

  Dongyi He, Bin Jiang, Xiangkai Wang, Yun Zhao, Hongjie Yan, Wai Ting Siok, Nizhuan Wang · 22. September 2026

  Electroencephalography (EEG) provides a noninvasive means of capturing emotion-related neural dynamics, yet reliable EEG emotion decoding lacks models that can both generalize to unseen individuals and populations while preserving neural interpretability. To address these challenges, EmoDiPyraTrans …

• Leakage-Safe Empirical Benchmarking of EEG-Based Machine Learning Pipelines for Dementia Classification

  Haitian Wang, Chamara Madarasingha, Redowan Mahmud, Aneesh Krishna, Ryu Takechi · 22. September 2026

  Electroencephalography (EEG) is a low-cost and non-invasive signal source for dementia screening, yet existing EEG-based studies remain difficult to compare because preprocessing, EEG segmentation, feature design, classifier choice, and validation protocols vary across studies and are often evaluate…

• Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces

  Beining Cao, Ziyi Zhao, Xiaowei Jiang, Daniel Leong, Yingtao Ren, Thomas Do, Yu-Cheng Fred Chang, Chin-Teng Lin · 22. September 2026

  Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision-making (ATDM) addresses this accuracy-time trade-…

• Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding

  Hongyu Zhu, Lin Chen, Jing Chen, Yuting Zhou, Mingsheng Shang · 22. September 2026

  Electroencephalography (EEG) provides non-invasive monitoring of brain activity and is widely used in emotion recognition, motor imagery and sleep staging. Although within-subject decoding has achieved considerable progress, cross-subject generalization remains a central challenge in practical appli…

• Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery

  Kevin Zhou, Sparsh Roy · 22. September 2026

  Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree about when their representations transfer to downstream tasks. We audit LaBraM and CBraMod on motor imagery under a validation-locked protocol in which…

• Adaptive Cortically Constrained EEG-Vision Alignment for Zero-Shot Brain-to-Image Retrieval

  Ye Wang, Haokun Ren, Wei Wu, Guoyin Wang, Zhuliang Yu, Hong Yu, Ke Liu · 22. September 2026

  Zero-shot brain-to-image retrieval requires robust alignment between noisy EEG responses and visual representations. Existing EEG-vision alignment methods often operate in sensor space and apply fixed visual supervision to all responses, ignoring both spatial mixing in scalp EEG and response-wise va…

• HDND: Hierarchical Dynamic Neural Decoding for Multilingual Word/Character Retrieval from Non-Invasive Brain Recordings

  Yueyang Li, Shuran Chen, Wai Ting Siok, Nizhuan Wang · 22. September 2026

  While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an unresolved challenge. Decoding individual words from non-invasive brain recordings is particularly difficult, as word-level neural evidence is weak, t…

• Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks

  Harshit Goyal · 22. September 2026

  Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolution, and paired EEG-image datasets remain small relative to modern generative-model training corpora. We present a reproducible single-subject baselin…