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

2010

Article

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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.

Author(s): Martens, SMM. and Leiva, JM.
Journal: Journal of Neural Engineering
Volume: 7
Number (issue): 2
Pages: 1-10
Year: 2010
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1088/1741-2560/7/2/026003
EPUB: 026003
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

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