Active Inference for Adaptive BCI: application to the P300 Speller
Résumé
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.
Domaines
Interface homme-machine [cs.HC]| Origine | Fichiers produits par l'(les) auteur(s) |
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