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arXiv · 1805.09109

Active Inference for Adaptive BCI: application to the P300 Speller

Jelena Mladenović·Jérémy Frey·Emmanuel Maby·Mateus Joffily·Fabien Lotte·Jeremie Mattout
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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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Jelena Mladenović, Jérémy Frey, Emmanuel Maby, Mateus Joffily, Fabien Lotte, Jeremie Mattout. 2018-05-22. Active Inference for Adaptive BCI: application to the P300 Speller. https://arxiv.org/abs/1805.09109

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