EEG Decoding

EEG: Electroencephalography

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21 papers in the last four weeks, up 200% on the four weeks before. 0.2% of all new papers.

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DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
Apr 20, 2026Zhiyuan Ma, Zeyuan Li, Zihao Qiu +6Cross-Subject EEG Decoding EEG Decoding
School of Biomedical Engineering, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China · School of Basic Medical Sciences and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China · Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China +1
PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
Apr 17, 2026Xiangkai Wang, Yun Zhao, Dongyi He +5Cross-Subject EEG Decoding Brain-Computer Interfaces
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots; Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission, Chongqing 401135, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing 401131, China · Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China +2
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Apr 16, 2026Jingyuan Wang, Zhihao Jia, Chenyu Liu +7Efficient Neural Network Inference EEG Decoding
1Xiamen University · 2Nanyang Technological University · Institute of Automation, Chinese Academy of Sciences +1
Variational Phasor Circuits for Phase-Native Brain-Computer Interface Classification
Mar 18, 2026Dibakar SigdelMI Classification Brain-Computer Interfaces
Mindverse Computing LLC, WA 98087
SHINE: Sequential Hierarchical Integration Network for EEG and MEG
Feb 27, 2026Xiran Xu, Yujie Yan, Songyi Li +4Electroencephalography Neural Processes
Speech and Hearing Research Center, School of Intelligence Science and Technology, Peking University, China · National Key Laboratory of General Artificial Intelligence, China · Center for BioMed-X Research, Academy for Advanced Interdisciplinary Studies, Peking University, China
Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance
Feb 19, 2026Guoxuan Ma, Yuan Zhong, Moyan Li +2Brain-Computer Interfaces EEG Decoding
Department of Biostatistics, University of Michigan, Ann Arbor MI 48109
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Feb 13, 2026Guoan Wang, Shihao Yang, Feng LiuEvolutionary Optimization EEG Decoding
Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ 08854, USA
AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
Dec 16, 2025Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon +1Electroencephalography Sleep Stage Classification
Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428 Jülich, Germany · Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands · Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428 Jülich, Germany
NeuroSketch: A Practical Design Recipe for Neural Decoding
Dec 10, 2025Gaorui Zhang, Zhizhang Yuan, Jialan Yang +4Brain-Computer Interfaces Neural Decoding
College of Computer Science and Technology, Zhejiang University · Shanghai Institute of Microsystem and Information Technology
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Nov 24, 2025Sanjeev Manivannan, Chandra Shekar LakshminarayanCross-Subject EEG Decoding MI Classification
Department of Biotechnology · Department of Data Science and Artificial Intelligence
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Oct 17, 2025Shuntaro Suzuki, Shunya Nagashima, Komei SugiuraMI Classification Brain-Computer Interfaces
Keio University
What your brain activity says about you: A review of neuropsychiatric disorders identified in resting-state and sleep EEG data
Oct 6, 2025J. E. M. Scanlon, A. Pelzer, M. Gharleghi +6Electroencephalography EEG Decoding
Fraunhofer Institute for Digital Media Technology, Branch Hearing, Speech and Audio Technology, Oldenburg, Germany · Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany · Department of Psychiatry and Psychotherapy, University Medical Center Schleswig-Holstein (UKSH), Kiel, Germany +1
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Sep 30, 2025Bipul Thapa, Biplov Paneru, Bishwash Paneru +1MI Classification Brain-Computer Interfaces
Department of Computer Science and Engineering, Kathmandu University, Kavre, Nepal · Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University, Bhaktapur, Nepal · Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur, Nepal
SingLEM: Single-Channel Large EEG Model
Sep 22, 2025Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue +5Representation Learning Self-Supervised Time Series Representation Learning
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Koganei-Shi 184–8588, Japan · Department of Electronics and Communication Engineering, National University of Mongolia, Ulaanbaatar, 14200 Mongolia
A foundation model with multi-variate parallel attention to generate neuronal activity
Jun 25, 2025Francesco Carzaniga, Michael Hersche, Abu Sebastian +2Electroencephalography EEG Decoding
IBM Research – Zurich, Rüschlikon, Switzerland · Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond
Feb 17, 2025Shreya Shukla, Jose Torres, Akshaj Murhekar +4EEG Decoding EEG-Based Visual Decoding
School of Information, The University of Texas at Austin, Austin, TX, USA · Institute of Applied Computer Science, Łód´z University of Technology, Łód´z, Poland

Apr 20, 2026, cs.AIJ/K move · Enter open · S save

DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding

Zhiyuan Ma, Zeyuan Li, Zihao Qiu, Jinhao Li+5

School of Biomedical Engineering, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China · School of Basic Medical Sciences and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China · Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China+1

Cross-Subject EEG Decoding EEG Decoding Attention Mechanisms Multiscale Time-Series Modeling

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In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent settings. One central challenge is that task-relevant EEG signals often follow different temporal organization patterns across tasks, while many existing methods rely on task-tailored architectural designs that introduce task-specific temporal inductive biases. This mismatch makes it difficult to adapt temporal modeling across tasks without changing the model configuration. To address these challenges, we propose DSAINet, an efficient dual-scale attentive interaction network for general EEG decoding. Specifically, DSAINet constructs shared spatiotemporal token representations from raw EEG signals and models diverse temporal dynamics through parallel convolutional branches at fine and coarse scales. The resulting representations are then adaptively refined by intra-branch attention to emphasize salient scale-specific patterns and by inter-branch attention to integrate task-relevant features across scales, followed by adaptive token aggregation to yield a compact representation for prediction. Extensive experiments on five downstream EEG decoding tasks across ten public datasets show that DSAINet consistently outperforms 13 representative baselines under strict subject-independent evaluation. Notably, this performance is achieved using the same architecture hyperparameters across datasets. Moreover, DSAINet achieves a favorable accuracy-efficiency trade-off with only about 77K trainable parameters and provides interpretable neurophysiological insights. The code is publicly available at https://github.com/zy0929/DSAINet.

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