3 papers
q-bio.NC2026
EEGDash: An open-source platform for machine learning on public neurophysiological data
Bruno Aristimunha, Aviv Dotan, Pierre Guetschel +7
Public neurophysiological datasets are increasingly accessible but remain hard to reuse: turning one into a trained model still takes thousands of lines of code for download, loadi…
q-bio.NC2026
Scaling and tuning to criticality in resting-state human magnetoencephalography
Irem Topal, Anna Poggialini, Marco Dal Maschio +3
From 1/f noise to neuronal avalanches, evidence of scaling in brain activity has been increasingly linked to tuning to or near criticality. The concept of scaling is intimately rel…
eess.SP2025
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +17
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…