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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.
Cited literature [4 references]
https://hal.inria.fr/hal-01796754
Contributor : Jérémy Frey <>
Submitted on : Monday, May 21, 2018 - 11:31:03 PM
Last modification on : Wednesday, November 20, 2019 - 3:12:23 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 8:06:05 PM
Contributor : Jérémy Frey <>
Submitted on : Monday, May 21, 2018 - 11:31:03 PM
Last modification on : Wednesday, November 20, 2019 - 3:12:23 AM
Document(s) archivé(s) le : Tuesday, September 25, 2018 - 8:06:05 PM
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