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.

EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model
Aug 13, 2025Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang +6Electroencephalography EEG Seizure Detection
School of Artificial Intelligence and Robotics, Xiamen University Malaysia · School of Energy and Chemical Engineering, Xiamen University Malaysia · Department of Biomedical Engineering, Universiti Malaya +3
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
KnowEEG: Explainable Knowledge Driven EEG Classification
May 1, 2025Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez +1Electroencephalography EEG Decoding
University of Bristol, Bristol, United Kingdom · Monash University, Melbourne, Victoria, Australia

Aug 13, 2025J/K move · Enter open · S save

EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang, Zixuan Ye+5

School of Artificial Intelligence and Robotics, Xiamen University Malaysia · School of Energy and Chemical Engineering, Xiamen University Malaysia · Department of Biomedical Engineering, Universiti Malaya+3

Electroencephalography EEG Seizure Detection Representation Learning Self-Supervised Time Series Representation Learning Diffusion Models Data-Efficient Learning+1

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Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learning meaningful representations from raw EEG signals remains challenging due to limited annotations, substantial inter-subject variability, and complex temporal dynamics. Recent EEG foundation models (FMs) have demonstrated promising performance through transformer-based architectures and large-scale self-supervised pretraining, yet they often incur substantial computational costs and exhibit diminishing returns with increasing model and dataset scale, limiting their practicality in clinical settings. To address these challenges, we propose EEGDM, a diffusion-based EEG representation learning framework. Specifically, EEGDM introduces a Structured State-Space Model for Diffusion Pretraining (SSMDP) that effectively captures long-range temporal dependencies through generative diffusion training on unlabeled EEG data. The representations are subsequently leveraged for downstream tasks via our Latent Fusion Module (LFM), which integrates multi-layer latent features of SSMDP. We evaluate EEGDM on three EEG benchmarks spanning EEG event classification (TUEV), seizure detection (CHB-MIT), and seizure classification (IIIC). Compared with existing state-of-the-art methods, including EEG FMs, EEGDM achieves competitive performance across diverse tasks and datasets, exceeding existing methods in most cases while requiring substantially fewer samples for both pretraining and downstream adaptation. These results demonstrate that diffusion-based learning can unlock more discriminative EEG representations, with direct implications for epilepsy diagnosis and management. Our source code and pretrained checkpoints are publicly available at: https://github.com/jhpuah/EEGDM.

Latest papers

EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

A foundation model with multi-variate parallel attention to generate neuronal activity

KnowEEG: Explainable Knowledge Driven EEG Classification