Active Inference for Adaptive BCI: application to the P300 Speller

Abstract : Adaptive Brain-Computer interfaces (BCIs) have shown to improve performance, however a general and flexible framework to implement adaptive features is still lacking. We appeal to a generic Bayesian approach, called Active Inference (AI), to infer user's intentions or states and act in a way that optimizes performance. In realistic P300-speller simulations, AI outperforms traditional algorithms with an increase in bit rate between 18% and 59%, while offering a possibility of unifying various adaptive implementations within one generic framework.
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Poster
International BCI meeting, May 2018, Asilomar, United States. 2018, 〈http://bcisociety.org/〉
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https://hal.inria.fr/hal-01796754
Contributeur : Jérémy Frey <>
Soumis le : lundi 21 mai 2018 - 23:31:03
Dernière modification le : mercredi 25 juillet 2018 - 14:32:02

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  • HAL Id : hal-01796754, version 1
  • ARXIV : 1805.09109

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Jelena Mladenović, Jérémy Frey, Emmanuel Maby, Mateus Joffily, Fabien Lotte, et al.. Active Inference for Adaptive BCI: application to the P300 Speller. International BCI meeting, May 2018, Asilomar, United States. 2018, 〈http://bcisociety.org/〉. 〈hal-01796754〉

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