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

2011

Article

ei


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.

Author(s): Martens, SMM. and Mooij, JM. and Hill, NJ. and Farquhar, J. and Schölkopf, B.
Journal: Neural Computation
Volume: 23
Number (issue): 1
Pages: 160-182
Year: 2011
Month: January
Day: 0

Department(s): Empirical Inference
Research Project(s): Brain-Computer Interfaces
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1162/NECO_a_00066
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{6440,
  title = {A graphical model framework for decoding in the visual ERP-based BCI speller},
  author = {Martens, SMM. and Mooij, JM. and Hill, NJ. and Farquhar, J. and Sch{\"o}lkopf, B.},
  journal = {Neural Computation},
  volume = {23},
  number = {1},
  pages = {160-182},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2011},
  month_numeric = {1}
}