arXiv AI July 14, 2026 By Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri, Belkacem Chikhaoui, Khadidja Henni

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

arXiv:2607. 11578v1 Announce Type: cross Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 26

Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets

arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...

By Meghal Dani, Stefanie Liebe

fine-tuning benchmarks

Hugging Face Trending Papers

Aug 25

Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets

EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...

fine-tuning benchmarks

arXiv AI

Jul 24

Multimodal Pretraining for Generalizable EEG Representation Learning

arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.

By Targol Bakhtiarvand, Jugal Kalita, Adham Atyabi

llms rag computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Sep 1

EEGDM: Learning EEG Representation with Latent Diffusion Model

EEGDM introduces a self‑supervised framework that uses latent diffusion models to generate EEG signals, moving beyond traditional masked reconstruction. The method employs an EEG encoder to produce a compact representation that conditions the diffusion denoising process, allowing joint optimization of encoder and generator. Experiments demonstrate that EEGDM can reconstruct high‑quality EEG, learn robust representations, and perform competitively on various downstream tasks.

By Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu

diffusion

arXiv Machine Learning

Sep 22

Attention-Enhanced Dual-Branch ConvNeXt-BiLSTM Network for Subject-Independent EEG Seizure Detection

arXiv:2609.22141v1 Announce Type: cross Abstract: Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples...

By Maimuna Chowdhury, Sk. Imran Hossain

computer-vision efficiency benchmarks

arXiv Machine Learning

4d ago

Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting

arXiv:2609.35876v1 Announce Type: new Abstract: Epileptic seizure forecasting aims to provide actionable warnings before seizure onset, yet patient-independent generalization and false-alarm control...

By Mason Dana, Khandaker Mamun Ahmed

One email a morning, machine-written

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Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets

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Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting