Optimal Transport Applied to Transfer Learning For P300 Detection

Nathalie T. H. Gayraud 1, 2 Alain Rakotomamonjy 3 Maureen Clerc 1, 2
1 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
3 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : Brain Computer Interfaces suffer from considerable cross-session and cross-subject variability, which makes it hard for classification methods to generalize. We introduce a transfer learning method based on regularized discrete optimal transport with class labels in the interest of enhancing the generalization capacity of state-of-the-art classification methods. We demonstrate the potential of this approach by applying it to offline cross-subject transfer learning for the P300-Speller paradigm. We also simulate an online experiment to assess the feasibility of our method. Results show that our method is comparable to-and sometimes even outperforms-session-dependent classification.
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Contributor : Alain Rakotomamonjy <>
Submitted on : Friday, April 26, 2019 - 10:59:40 PM
Last modification on : Friday, November 1, 2019 - 4:46:06 PM


  • HAL Id : hal-02112774, version 1


Nathalie T. H. Gayraud, Alain Rakotomamonjy, Maureen Clerc. Optimal Transport Applied to Transfer Learning For P300 Detection. BCI 2017 - 7th Graz Brain-Computer Interface Conference, Sep 2017, Graz, Austria. pp.6. ⟨hal-02112774⟩



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