arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
By Jonathan F. Carter, Lionel Tarassenko
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.
By Arvasu Kulkarni, Aditya Ray Mishra, Mahir Jain, Parshva Runwal, Lakshya Saini, Siddharth Panwar, Sandeep Singh
arXiv:2606. 19888v1 Announce Type: cross Abstract: Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data.
By Feng Wu, Harsh Deep, Eric Lehman, Sanyam Kapoor, Guoshuai Zhao, Rahul Krishnan, Gari Clifford, Li-wei H Lehman
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.
By Minghao Xiao
arXiv:2505. 23869v4 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a compression process.
By M. S\"uzen
arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.
By Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura