A generative model approach for decoding in the visual event-related 
potential-based brain-computer interface speller

Martens, SMM; Leiva, JM

doi:10.1088/1741-2560/7/2/026003

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A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller

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Martens, SMM
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Leiva, JM
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://iopscience.iop.org/article/10.1088/1741-2560/7/2/026003/pdf
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Citation

Martens, S., & Leiva, J. (2010). A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller. Journal of Neural Engineering, 7(2): 026003, pp. 1-10. doi:10.1088/1741-2560/7/2/026003.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C0A6-4

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.