Electroencephalography

Also known as EEG

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

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

MedMamba: Recasting Mamba for Medical Time Series Classification
Apr 17, 2026ZhengXiao He, Huayu Li, Xiwen Chen +4Mamba Electroencephalography
Department of Electrical & Computer Engineering at the University of Arizona, Tucson, AZ, USA
MLE-Toolbox: An Open-Source Toolbox for Comprehensive EEG and MEG Data Analysis
Apr 8, 2026Xiaobo LiuMagnetoencephalography Neuroimaging
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
Convolutional Neural Network and Adversarial Autoencoder in EEG images classification
Apr 5, 2026Albert Nasybullin, Semen KurkinEEG Decoding MI Classification
Faculty of Computer Science and Engineering, Innopolis University, Innopolis, Russia · Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding
Apr 3, 2026Yihang Dong, Changhong Jing, Shuqiang WangBrain-Computer Interfaces Electroencephalography
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. · University of Chinese Academy of Sciences, Beijing, China.
Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals
Mar 31, 2026Ferdaus Anam Jibon, Fazlul Hasan Siddiqui, F. Deeba +1EEG Seizure Detection Electroencephalography
Department of Computer Science and Engineering, Dhaka University of Engineering & Technology (DUET), Gazipur. · Department of Computer Science and Engineering, IUBAT-International University of Business Agriculture and Technology, Uttara, Dhaka-1230. · Department of Physics, Dhaka University of Engineering & Technology (DUET), Gazipur. +1
WearBCI Dataset: Understanding and Benchmarking Real-World Wearable Brain-Computer Interfaces Signals
Mar 29, 2026Haoxian Liu, Hengle Jiang, Lanxuan Hong +1EEG Artifact Removal Brain-Computer Interfaces
Hong Kong University of Science and Technology, Hong Kong SAR, China
Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
Mar 26, 2026Mohamed Mahdi, Asma BaghdadiEEG Seizure Detection Electroencephalography
Information and Communication Engineering Department National Engineering School of Tunis Tunis El Manar University · REGIM-Lab, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia
Preventing Data Leakage in EEG-Based Survival Prediction: A Two-Stage Embedding and Transformer Framework
Mar 26, 2026Yixin Zhou, Zhixiang Liu, Vladimir I. Zadorozhny +1Temporal Data Leakage Clinical Outcome Prediction
University of Pittsburgh School of Computing and Information Pittsburgh, PA 15213 · University of Pittsburgh Department of Critical Care Medicine Pittsburgh, PA 15213
LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling
Mar 19, 2026Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson +2EEG Foundation Models Electroencephalography
Integrated Systems Laboratory, ETH Zürich, Zürich, Switzerland · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · DEI, University of Bologna, Bologna, Italy
Real-Time Decoding of Movement Onset and Offset for Brain-Controlled Rehabilitation Exoskeleton
Mar 17, 2026Kanishka Mitra, Satyam Kumar, Frigyes Samuel Racz +3Brain-Computer Interfaces Assistive Robotics
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. · Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA. · Department of Neurology, The University of Texas at Austin, Austin, TX, USA. +3
Fronto-parietal and fronto-temporal EEG coherence as predictive neuromarkers of transcutaneous auricular vagus nerve stimulation response in treatment-resistant schizophrenia: A machine learning study
Mar 14, 2026Yapeng Cui, Ruoxi Yun, Shumin Zhang +16Clinical Outcome Prediction Clinical Prediction
Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xian, 710126, China. · Wearable BCI and Intelligent Rehabilitation Innovation Lab, Guangzhou Institute of Technology, Xidian University, Guangzhou, 510530, China. · School of Second Clinical Medicine/The Second Affiliated Hospital, Yunnan University of Chinese Medicine, Kunming, 650500, China. +1
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
Mar 11, 2026Ziwei Wang, Zhentao He, Xingyi He +6Synthetic Data Generation Brain-Computer Interfaces
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China · Zhongguancun Academy, Beijing, China
The Deep-Match Framework for Event-Related Potential Detection in EEG
Mar 11, 2026Marek Zylinski, Bartosz Tomasz Smigielski, Gerard CybulskiElectroencephalography EEG Decoding
Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
Mar 11, 2026Ahmad Bdeir, Johannes Burchert, Tom Hanika +2Cross-Subject EEG Decoding Electroencephalography
Data Science Group, Universität Hildesheim · ISMLL, Universität Hildesheim
Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
Mar 11, 2026Renwei MengCross-Subject EEG Decoding Electroencephalography
Anhui University, Hefei, China
Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
Mar 11, 2026Viktorija Poļaka, Ivo Pascal de Jong, Andreea Ioana SburleaSynthetic Data Augmentation Variational Autoencoders
Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
Self-Supervised Evolutionary Learning of Neurodynamic Progression and Identity Manifolds from EEG During Safety-Critical Decision Making
Mar 7, 2026Xiaoshan Zhou, Carol C. Menassa, Vineet R. KamatSelf-Supervised Learning Electroencephalography
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109-2125. · Professor, Dept. of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109-2125.
Stringology-Based Motif Discovery from EEG Signals: an ADHD Case Study
Mar 3, 2026Anat Dahan, Samah GhazawiElectroencephalography Mental Health
Braude College of Engineering, Karmiel, Israel
EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning
Mar 3, 2026Fengcheng Wu, Zhenxi Song, Guoyang Xu +4Electroencephalography Dynamic Graph Learning
Harbin Institute of Technology, Shenzhen, China · Institute of Neurological Diseases, Shenzhen Bay Laboratory, Shenzhen · Institute of Neurological Diseases, Shenzhen Bay Laboratory, Shenzhen, China
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
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
Feb 27, 2026Dongyi He, Bin Jiang, Kecheng Feng +5Electroencephalography
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Feb 26, 2026Haohui Jia, Zheng Chen, Lingwei Zhu +6Latent Dynamics Modeling Electroencephalography
Information Science and Techinology, Hokkaido University, Japan · SANKEN, The University of Osaka, Japan · Great Bay University, China +1
Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
Feb 26, 2026Hanning Guo, Hanwen Bi, Farah Abdellatif +4Functional Magnetic Resonance Imaging Multimodal Foundation Models
INM-4, Forschungszentrum Jülich, Germany · Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Germany · INM-7, Forschungszentrum Jülich, Germany +6
NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning
Feb 24, 2026Guoan Wang, Shihao Yang, Jun-en Ding +2EEG Foundation Models Electroencephalography
Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, New Jersey, USA.
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
Feb 20, 2026Yicheng Feng, Hairong Chen, Ziyu Jia +2Alzheimer's Disease Classification Latent Dynamics Modeling
Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Institute of Information Science, Beijing Jiaotong University, Beijing, China · College of Computing and Data Science, Nanyang Technological University, Singapore +3
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
Feb 17, 2026Chenda Duan, Yipeng Zhang, Sotaro Kanai +9Electroencephalography
UCLA Samueli School of Engineering · UCLA Mattel Children’s Hospital, David Geffen School of Medicine · Children’s Hospital of Michigan, Wayne State University School of Medicine
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
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Oct 13, 2025Jun-En Ding, Anna Zilverstand, Shihao Yang +2Alzheimer's Disease Electroencephalography
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA · Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA · Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University, Taipei City, Taiwan
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
EEGDM: Learning EEG Representation with Latent Diffusion Model
Aug 28, 2025Shaocong Wang, Tong Liu, Yihan Li +6Electroencephalography EEG Signal Generation
Tsinghua University · Tianjin University

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MedMamba: Recasting Mamba for Medical Time Series Classification

ZhengXiao He, Huayu Li, Xiwen Chen, Janet M Roveda+3

Department of Electrical & Computer Engineering at the University of Arizona, Tucson, AZ, USA

Mamba Electroencephalography Time Series Classification Electrocardiogram Classification Multiscale Time-Series Modeling

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Medical time series, such as electrocardiograms (ECG) and electroencephalograms (EEG), exhibit complex temporal dynamics and structured cross-channel dependencies, posing fundamental challenges for automated analysis. Conventional convolutional and recurrent models struggle to capture long-range dependencies, while Transformer-based approaches incur quadratic complexity and often introduce redundant interactions that are misaligned with the intrinsic structure of physiological signals. To address these limitations, we propose MedMamba, a principle-driven multi-scale bidirectional state space architecture tailored for medical time series classification. Our design is guided by three key inductive biases of physiological signals: spatial centralization, multi-timescale temporal composition, and non-causal contextual dependency. These principles are instantiated through a lightweight channel-mixing module for cross-channel reparameterization, multi-scale convolutional tokenization for temporal decomposition, and bidirectional Mamba blocks for efficient global context modeling with linear complexity. Extensive experiments on six benchmark datasets spanning EEG, ECG, and human activity signals demonstrate that MedMamba consistently outperforms state-of-the-art methods across diverse modalities. Notably, it achieves 85.97% accuracy on PTB and establishes new state-of-the-art performance on the challenging ADFTD dataset (54.72% accuracy and 52.01% F1-score). Strong results on long-sequence benchmarks, such as SleepEDF, further validate its capability in modeling long-range dependencies. Moreover, MedMamba achieves a speedup of 4.6x in inference, highlighting its practicality for real-time clinical deployment. These results suggest that principle-guided state space modeling offers an effective and scalable alternative to Transformer-based approaches for medical time series analysis.