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.
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...
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.
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.
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...
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...
arXiv:2607. 16296v1 Announce Type: cross Abstract: Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices.
arXiv:2506.20354v3 Announce Type: replace-cross Abstract: Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, p...
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.
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.
arXiv:2606. 08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility.