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

Martens, S., Mooij, J., Hill, N., Farquhar, J., & Schölkopf, B. (2011). A graphical model framework for decoding in the visual ERP-based BCI speller. Neural computation, 23(1), 160-182. doi:10.1162/NECO_a_00066.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BCB8-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-BCC8-A
Genre: Journal Article

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 Creators:
Martens, SMM, Author              
Mooij, JM1, 2, Author              
Hill, NJ1, 2, Author              
Farquhar, J, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2011-01
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1162/NECO_a_00066
BibTex Citekey: 6440
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 23 (1) Sequence Number: - Start / End Page: 160 - 182 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591