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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C0A6-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-76E0-C
Genre: Journal Article

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 Creators:
Martens, SMM1, 2, Author              
Leiva, JM1, 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: 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.

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 Dates: 2010-04
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1088/1741-2560/7/2/026003
BibTex Citekey: 6262
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Title: Journal of Neural Engineering
Source Genre: Journal
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Publ. Info: Bristol : Institute of Physics Publishing
Pages: - Volume / Issue: 7 (2) Sequence Number: 026003 Start / End Page: 1 - 10 Identifier: ISSN: 1741-2552
CoNE: https://pure.mpg.de/cone/journals/resource/17412552