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arXiv · 1403.3628

Mixed-norm Regularization for Brain Decoding

Rémi Flamary·Nisrine Jrad·Ronald Phlypo·Marco Congedo·Alain Rakotomamonjy
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Abstract

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.

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BibTeXRIS

Rémi Flamary, Nisrine Jrad, Ronald Phlypo, Marco Congedo, Alain Rakotomamonjy. 2014-03-14. Mixed-norm Regularization for Brain Decoding. https://arxiv.org/abs/1403.3628

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