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Learning Regularization Structure for Biosignal Template Estimation
Yonathan Murin, Alexandre Gramfort
Estimating event-locked templates from bio-signal recordings via regularized least-squares requires choosing both the regularization structure and its magnitude, choices that are t…
Evaluating the structure of cognitive tasks with transfer learning
Bruno Aristimunha, Raphael Y. de Camargo, Walter H. Lopez Pinaya +3
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this chall…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
Cédric Allain, Alexandre Gramfort, Thomas Moreau
The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal…
Spectral independent component analysis with noise modeling for M/EEG source separation
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Background: Independent Component Analysis (ICA) is a widespread tool for exploration and denoising of electroencephalography (EEG) or magnetoencephalography (MEG) signals. In its…
Manifold-regression to predict from MEG/EEG brain signals without source modeling
David Sabbagh, Pierre Ablin, Gael Varoquaux +2
Magnetoencephalography and electroencephalography (M/EEG) can reveal neuronal dynamics non-invasively in real-time and are therefore appreciated methods in medicine and neuroscienc…