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Optimal Transport Applied to Transfer Learning For P300 Detection

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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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Dates and versions

hal-01556603 , version 1 (05-07-2017)

Identifiers

  • 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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