A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller.
There is a strong tendency towards discriminative approaches in brain-computer interface (BCI) research. We argue that generative model-based approaches are worth pursuing and propose a simple generative model for the visual ERP-based BCI speller which incorporates prior knowledge about the brain signals. We show that the proposed generative method needs less training data to reach a given letter prediction performance than the state of the art discriminative approaches.
Martens
S M M
SM
Empirical Inference Department, Max Planck Institute for Biological Cybernetics, Tübingen, Germany. smm.martens@gmail.com
Leiva
J M
JM
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D005074:Evoked Potentials, Visual; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014584:User-Computer Interface; D014956:Writing