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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.
@article{6262, title = {A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller}, journal = {Journal of Neural Engineering}, abstract = {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.}, volume = {7}, number = {2}, pages = {1-10}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = apr, year = {2010}, slug = {6262}, author = {Martens, SMM. and Leiva, JM.}, month_numeric = {4} }