Mixed-norm Regularization for Brain Decoding

Rémi Flamary (1) , Nisrine Jrad (2) , Ronald Phlypo (2) , Marco Congedo (2) , Alain Rakotomamonjy (1)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 GIPSA-lab - Grenoble Images Parole Signal Automatique

This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multi-task learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multi-task approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

Mots clés: multitask learning; SVM; sensor selection; BCI

Domaines: Apprentissage [cs.LG]

Rémi Flamary, Nisrine Jrad, Ronald Phlypo, Marco Congedo, Alain Rakotomamonjy. Mixed-norm Regularization for Brain Decoding. Computational and Mathematical Methods in Medicine, 2014, http://www.hindawi.com/journals/cmmm/. ⟨hal-00708243v1⟩