MANAS-2 is a new EEG foundation model that integrates a Raw‑Band Hybrid masked autoencoder with a physics‑motivated Constrained Reconstruction (ConRec) regularizer. ConRec penalizes RMS energy differences in short temporal windows, guiding the encoder toward oscillatory‑envelope organization. Across seven held‑out EEG datasets, adding ConRec improves spectral‑power recovery (R² from 0.860 to 0.906) and band‑energy dynamics (R² from 0.283 to 0.354), while maintaining strong temporal waveform recoverability and outperforming leading EEG models on downstream tasks.
arXiv:2608. 01898v1 Announce Type: new Abstract: Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance.
arXiv:2605. 29263v2 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased view of distributed scalp activity.
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:2607. 09543v1 Announce Type: new Abstract: Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications.
arXiv:2608. 13676v1 Announce Type: new Abstract: Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG.
arXiv:2605. 29263v3 Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased sampling of the scalp potential field.
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
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:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.
arXiv:2607. 21384v1 Announce Type: new Abstract: Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks.