A review of classification algorithms for EEG-based brain–computer interfaces

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A review of classification algorithms for EEG-based brain–computer interfaces
Authors: Fabien Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi
Citation: Journal of Neural Engineering 4 : missing pages. 2007
Database(s): Google Scholar cites PubMed (PMID/17409472)
DOI: Define doi.
Link(s): http://hal.archives-ouvertes.fr/docs/00/13/49/50/PDF/article.pdf
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A review of classification algorithms for EEG-based brain–computer interfaces (preprint titled A review of classification algorithms for EEG-based brain-computer interfaces)

[edit] Features

  • EEG signal amplitude[1]
  • Band powers[2]
  • Power spectral density[3][4]
  • autoregressive and adaptive autoregressive parameters
  • Time-frequency features
  • Inverse-model based features

[edit] References

  1. BCI competition 2003-data set iib: support vector machines for the P300 speller paradigm
  2. EEG-based discrimination between imagination of right and left hand movement
  3. HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems
  4. Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project
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