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Optimal Transport Applied to Transfer Learning For P300 Detection

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 : Nathalie Thérèse Hélène Gayraud Connect in order to contact the contributor
Submitted on : Wednesday, July 5, 2017 - 12:17:27 PM
Last modification on : Tuesday, October 19, 2021 - 4:17:02 PM
Long-term archiving on: : Tuesday, January 23, 2018 - 8:05:00 PM


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  • HAL Id : hal-01556603, 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-01556603⟩



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