Electroencephalography

Also known as EEG

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Overview

Electroencephalography measures brain activity through electrodes on the scalp or, in intracranial recordings, within the brain, enabling study of cognition and applications such as seizure monitoring, sleep staging, and brain-computer interfaces. Methods range from spectral and Riemannian features to convolutional and recurrent networks, graph models, Transformers, and state-space models; self-supervised learning and generative methods aim to use large amounts of unlabeled data. Foundation models are being tested across clinical classification, motor imagery, and event detection, but their advantages over task-specific models are not consistent. A central challenge is reliable generalization across people, hospitals, and electrode montages, alongside evaluation that prevents data leakage and measures clinical usefulness rather than accuracy alone.

Latest papers

CardsList

183 papers28 papers in the last four weeks, up 56% on the four weeks before. 0.2% of all new papers.

  1. REVBRAIN: Real-time Probabilistic EEG Forecasting Using Residual Vector Quantization

    Oct 28, 2026Hanna Pankka, Jaakko Lehtinen, Timo RoineTime Series ForecastingProbabilistic Time Series Forecasting

    Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland · Department of Computer Science, Aalto University School of Science, Finland · NVIDIA, Finland +1

  2. Normative Deviation Modeling for EEG-based Depression Detection

    Oct 27, 2026Wan Jiang, Jingqi Cheng, Hui Zhang +5Depression DetectionElectroencephalography

    Hefei University of Technology · Anhui University

  3. MM-Net: A Multimodal Multi-Task Network for Sleep Staging and Sleep Apnea Assessment After Ischemic Stroke

    Oct 27, 2026Md Wahiduzzaman Suva, Esm E Moula Chowdhury Abha, Md Imtiaj Alam Sajin +2Sleep Stage ClassificationMultimodal Learning

    Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, 1229, Bangladesh · AI and Big Data Department, Woosong University, Daejeon, Republic of Korea

  4. OneEEG: A Unified Signal-Visual Multimodal Language Model with Task-Aware Mixture of Projectors for Multi-Task EEG Understanding

    Oct 26, 2026Jiacheng Zhang, Xueyi Zhang, Xin Lu +2ElectroencephalographyMultimodal Large Language Models

    School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China · Language, Intelligence, and Machine Center, Shenzhen Loop Area Institute, Shenzhen, China · School of Computing, National University of Singapore, Singapore +2

  5. NOMAD-EEG: Native Omni-Montage Adaptive Diffusion

    Oct 26, 2026Abdulkader Helwan, Khadidja Henni, Lina Abou-AbbasElectroencephalographyEEG Foundation Models

    Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon · Institute of Applied Artificial Intelligence, TÉLUQ University, Montreal, Canada

  6. U-NeXt: General-purpose event detection for neonatal EEG

    Oct 23, 2026Robert Hogan, Sean R. Mathieson, Aurel Luca +4ElectroencephalographyEEG Seizure Detection

    CergenX Ltd, Cork, Ireland · INFANT Research Centre, University College Cork, Ireland · Department of Paediatrics and Child Health, University College Cork, Ireland

  7. Exact Spectral Uncertainty Decomposition for Linear-Gaussian Bayesian M/EEG Inverse Problems

    Oct 21, 2026Ismail Huseynov, Stefan HaufeUncertainty QuantificationUncertainty Decomposition

    Physikalisch-Technische Bundesanstalt, Berlin, Germany · Technische Universitat Berlin, Berlin, Germany · Charité–Universitätsmedizin Berlin, Berlin, Germany

  8. CNN-based classifier for subject-independent MBTI Extraversion/Introversion preference classification from short-time EEG signals

    Oct 20, 2026Filip Postepski, Grzegorz M. WojcikConvolutional Neural NetworksCross-Subject EEG Decoding

    Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science and Mathematics, Maria Curie-Sk lodowska University, Akademicka 9, Lublin, 20-031, Lubelskie, Poland.

  9. EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks

    Oct 20, 2026Krzysztof Wrobel, Grzegorz M. Wojcik, Filip Postepski +3ClassificationEEG Decoding

    Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Skłodowska University, Akademicka 9, 20-031 Lublin, Poland · Institute of Psychology, SWPS University, Chodakowska 19/31, 03-815 Warsaw, Poland

  10. Rethinking EEG Spatial Super-Resolution as Dynamic State Decomposition and Filtering

    Oct 17, 2026Hongjun Liu, Leyu Zhou, Chang Ge +2ElectroencephalographyState Space Models

    University of Science and Technology Beijing

  11. Seeing Seizures: A Large-Scale Empirical Study of Video and EEG Deep Learning for Automated Rodent Seizure Detection and Severity Grading

    Oct 15, 2026Alloy Das, Shreyan Ganguly, Nyzil Massey +4EEG Seizure DetectionElectroencephalography

    Iowa State University, Ames, IA, USA · Arkansas State University, Jonesboro, AR, USA

  12. NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection

    Oct 14, 2026Yaozhi Wen, Kun Zhao, Wei Wei +7ElectroencephalographyBrain-Computer Interfaces

    Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences · School of Future Technology, University of Chinese Academy of Sciences · Zhongguancun Academy +3

  13. Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars

    Oct 12, 2026Camilo Andrés Martínez Martínez, Olaf Dünkel, Kartik Teotia +3EEG-Based Visual DecodingEEG Decoding

    Max Planck Institute for Informatics, Saarland Informatics Campus · German Research Center for Artificial Intelligence (DFKI)

  14. Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals

    Oct 12, 2026Ramya Sundaram, Muhammad Aslam Jarwar, M Uma +2EEG Seizure DetectionMultimodal Classification

    School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield and University of Strathclyde, Glasgow, United Kingdom. · School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield, United Kingdom · Department of Cmputational Intelligence, SRM Institute of Science and Technology,Kattankulathur-603203, India. +2

  15. HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning

    Oct 12, 2026Xin Liu, Feng Lu, Wei LiElectroencephalographySelf-Supervised Pre-Training

  16. Spectral Considerations on Unsupervised Time-domain EEG Representation Learning with Deep Networks

    Oct 12, 2026David Aquilué-Llorens, Abel Puigseslloses, Angel Blanco +2Unsupervised Representation LearningAutoencoders

    Starlab Barcelona S.L., Neuroscience Business Unit, Barcelona, Spain · Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain

  17. Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

    Oct 8, 2026John M. O'Toole, Sean R. Mathieson, Aurel Luca +4Electroencephalography

    CergenX Ltd, Cork, Ireland · INFANT Research Centre, University College Cork, Ireland · Department of Paediatrics and Child Health, University College Cork, Ireland

  18. Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

    Oct 7, 2026Rita Huan-Ting Peng, Nhat BuiLatent Dynamics ModelingElectroencephalography

    University of Illinois Urbana-Champaign, Urbana, IL, USA · Carle Foundation Hospital, Urbana, IL, USA

  19. A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation

    Oct 7, 2026Lu Wang-Nöth, Hai Huang, Philipp Heiler +3EEG Artifact RemovalElectroencephalography

    Institute for Applied Computer Science, University of the Bundeswehr Munich, Munich, Germany · brainboost GmbH, Munich, Germany · Graduate School of Engineering Science, The University of Osaka, Osaka, Japan +1

  20. Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals

    Oct 6, 2026Jaedong HwangElectroencephalographyEEG Signal Generation

    Massachusetts Institute of Technology

  21. CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imaging

    Oct 6, 2026Shuntaro Suzuki, Yuiga Wada, Komei SugiuraElectroencephalographyNeuroimaging

    Keio University

  22. SpecBraM: What Should an EEG Foundation Model Predict? Masked Band-Power Prediction versus Waveform Reconstruction

    Oct 5, 2026Peng Xie, Yequan Bie, Jianda Mao +1ElectroencephalographySleep Stage Classification

    The Hong Kong University of Science and Technology

  23. Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging

    Oct 5, 2026Marco Morik, Jesse Palarus, Carmen Vidaurre +2Electroencephalography

    Berlin Institute for the Foundations of Learning and Data, Germany · Machine Learning Group, Technische Universität Berlin, Germany · Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua, Spain +4

  24. COVER: Learning to Accept More in Selective Sleep Staging

    Oct 2, 2026Yukai Song, Yangfan Deng, Jijun Yin +2Selective PredictionSleep Stage Classification

    Department of Electrical and Computer Engineering, University of Pittsburgh · Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA · Department of Electrical and Computer Engineering, University of Maryland, College Park +1

  25. CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

    Oct 1, 2026Jiazhen Hong, Xiaotian Zhou, Zihao Ding +2ElectroencephalographyBrain-Computer Interfaces

    Department of Electrical and Computer Engineering, Rutgers University Piscataway, NJ 08854, USA

  26. Fusion techniques of time frequency-based images to predict the outcome of rTMS depression therapy

    Sep 30, 2026Wael Korani, Md Fahimul Kabir Chowdhury, Mohammed Aledhari +2ElectroencephalographyClinical Outcome Prediction

  27. NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory

    Sep 29, 2026Yifan Wang, Haiping Liu, Yang Cui +13Memory-Augmented Neural NetworksElectroencephalography

    University of Manchester · ETH Zürich · Shanghai Jiao Tong University +5

  28. GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling

    Sep 29, 2026Haohui Jia, Zheng Chen, Jathurshan Pradeepkumar +4ElectroencephalographyFunctional Connectivity

    Faculty of Information Science and Technology, Hokkaido University, Japan · SANKEN, The University of Osaka, Japan · Department of Computer Science, University of Illinois Urbana-Champaign, USA +1

  29. High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching

    Sep 29, 2026Om Roy, Yashar Moshfeghi, Keith Malcolm SmithDynamic Functional ConnectivityFunctional Connectivity

    University of Strathclyde

  30. NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics

    Sep 29, 2026Yi Cui, Tong Zhao, Jiaxin Lei +5ElectroencephalographyNeural Posterior Estimation

    Gnosis Neurodynamics Co. Ltd, Room 202, West Area, Building North 1, No. 9 Yard, Shengmingyuan Road, Changping District, Beijing, China · School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China · University of California, Davis, department of psychology +2