papers
Publications (3)
cs.LG2026
CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
Gabriel Mahuas, Victoria Shevchenko, Ugo Tanielian +2
Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converg…
cs.LG2026
ReBaPL: Repulsive Bayesian Prompt Learning
Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma +3
Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to…
q-bio.NC2026
The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail
Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell +2
Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data. We posit that these models should capture each subject's cognitive performance from their f…