Active Inference for Adaptive BCI: application to the P300 Speller

Jelena Mladenović; Jérémy Frey; Emmanuel Maby; Mateus Joffily; Fabien Lotte; Jeremie Mattout

Active Inference for Adaptive BCI: application to the P300 Speller

Jelena Mladenović ^{1, 2, *} Jérémy Frey ³ Emmanuel Maby ² Mateus Joffily ⁴ Fabien Lotte ¹ Jeremie Mattout ²

* Corresponding author

1 Potioc - Popular interaction with 3d content

LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest

2 CRNL - Centre de recherche en neurosciences de Lyon

3 Ullo

4 GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique

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.

Keywords : P300 Speller Active Inference BCI EEG

Document type :

Poster communications

Domain :

Computer Science [cs] / Human-Computer Interaction [cs.HC]

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https://hal.inria.fr/hal-01796754
Contributor : Jérémy Frey <>
Submitted on : Monday, May 21, 2018 - 11:31:03 PM
Last modification on : Wednesday, October 31, 2018 - 12:24:13 PM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 8:06:05 PM

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Active Inference for Adaptive BCI: application to the P300 Speller

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