arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.
arXiv:2607. 09543v1 Announce Type: new Abstract: Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications.
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.
The paper introduces BraVista, a visual-language framework that transforms multichannel EEG signals into structured images and uses instruction‑conditioned vision‑language models for unified multi‑task EEG decoding. By continuing post‑training of a general‑domain VLM, BraVista adapts to neural signals without large‑scale EEG‑specific pretraining, achieving strong performance across sleep staging, emotion recognition, cognitive workload classification, and abnormal EEG detection. Analyses show that the EEG‑to‑image representation is crucial and that the model’s performance degrades gradually with added noise, indicating reliance on genuine EEG information.
arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.
arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging du...
Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due to their high temporal complexity, substantial...
arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...
arXiv:2608. 11656v1 Announce Type: new Abstract: Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets.
arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.
LEAD is a gated temporal‑spatial Transformer foundation model designed for EEG‑based Alzheimer's disease detection. It was trained on the world’s largest EEG‑AD corpus of 2,238 subjects and uses a subject‑regularized strategy and medical contrastive learning across 13 datasets. LEAD outperforms existing methods on five downstream AD datasets, achieving the best average ranking across 20 evaluations.
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...