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arXiv Machine Learning By Minsu Kim, Ye-Sung Kim, Hyeseong Jeon, Wooseok Hyung, Joshua Lee, Chang-Hwan Im

SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction

Read the original on arXiv Machine Learning →

SPERA is a new EEG foundation model that predicts in latent space using a joint-embedding predictive architecture. It incorporates a Legendre-polynomial spatial prior into attention to handle varying scalp electrode geometries, and adds factorized temporal and spatial attention with periodic full-attention blocks, plus a relational spectral regularizer to align latent similarity with spectral views. Trained on about 80,000 hours of EEG from 29,048 subjects across 106 datasets, SPERA achieves the highest average balanced accuracy on nine downstream tasks in clinical, cognitive, and BCI domains, while demonstrating strong parameter efficiency and robustness to different recording conditions.

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 Machine Learning.

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