A graphical model framework for decoding in the visual ERP-based BCI speller.

TitleA graphical model framework for decoding in the visual ERP-based BCI speller.
Publication TypeJournal Article
Year of Publication2011
AuthorsMartens, SMM, Mooij, JM, Hill, N Jeremy, Farquhar, J, Schölkopf, B
JournalNeural Comput
Volume23
Issue1
Pagination160-82
Date Published01/2011
ISSN1530-888X
KeywordsArtificial Intelligence, Computer User Training, Discrimination Learning, Electroencephalography, Evoked Potentials, Evoked Potentials, Visual, Humans, Language, Models, Neurological, Models, Theoretical, Reading, Signal Processing, Computer-Assisted, User-Computer Interface, Visual Cortex, Visual Perception
Abstract

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

URLhttp://www.ncbi.nlm.nih.gov/pubmed/20964540
DOI10.1162/NECO_a_00066
Alternate JournalNeural Comput
PubMed ID20964540

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