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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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https://hal.inria.fr/hal-01556603
Contributor : Nathalie Thérèse Hélène Gayraud <>
Submitted on : Wednesday, July 5, 2017 - 12:17:27 PM
Last modification on : Monday, October 12, 2020 - 10:28:54 AM
Long-term archiving on: : Tuesday, January 23, 2018 - 8:05:00 PM

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  • HAL Id : hal-01556603, version 1

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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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