DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings. We present DiffEEG, a 9.6M-parameter self-supervised foundation model that addresses both limitations through denoising diffusion pre-training and reinforcement learning (RL)-based fine-tuning. Pre-trained on 1.3M unlabeled segments from the Temple University Hospital Seizure Corpus (TUHSZ), DiffEEG learns generic neural representations via a 1D U-Net with multi-head self-attention. For downstream ada
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorAbdulkader Helwan →
DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
- Linked via arxiv authorLina Abou-Abbas →
DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
- Linked via arxiv authorHussein El Amouri →
DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
- Linked via arxiv authorBelkacem Chikhaoui →
DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
- Linked via arxiv authorKhadidja Henni →
DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations