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

arXiv:2508.14086 · cs.LG · Submitted 2025-08-13 · Read on arXiv

cs.LG

Submitted: 2025-08-13

Updated: 2026-10-05

Comments: Accepted by IEEE JBHI (2026; DOI: 10.1109/JBHI.2026.3737616) 12 Pages

DOI: 10.1109/JBHI.2026.3737616

Code: https://github.com/jhpuah/EEGDM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 2× in this paper · explained in 10 episodes
• Representation Learning — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Foundation Models (FMs) — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 3 episodes

Sources

• Multi-Channel Differential Transformer for Cross-Domain Sleep Stage Classification with Heterogeneous EEG and EOG
• EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
• Diffusion Models Beat GANs on Image Classification
• Large Cognition Model: Towards Pretrained EEG Foundation Model
• DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
• Classifier-Free Diffusion Guidance
• Efficiently Modeling Long Sequences with Structured State Spaces
• Self-supervised EEG Representation Learning for Automatic Sleep Staging
• Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

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