Asynchronous, adaptive BCI using movement imagination training and rest-state inference

Siamac Fazli, Márton Danóczy, Motoaki Kawanabe, Florin Popescu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The current study introduces an adaptive Bayesian learning scheme which discriminates between left hand movement imagination, right hand movement imagination and idle (i.e. "no-command") state in an EEG Brain Computer Interface. Unlike previous BCI designs using minimal training, the user does not have to continuously imagine a movement in order to control a cursor. Rather, the cursor reacts meaningfully only when a trained movement imagination is produced. The algorithmic approach was to compute Gaussian probability distributions in log-variance of main Common Spatial Patterns for each movement class, infer from these a prior distribution of idle-class, and allow each distribution to adapt during feedback BCI performance. By producing a markedly different but complexity constrained partition of feature space than with LDA classifiers, allowing the classifier to adapt and introducing an intermediary state driven by the classifier output through a dynamic control law, 90% level classification accuracy was achieved with less than 5 seconds activation time from cued onset.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
Pages85-90
Number of pages6
Publication statusPublished - 2008 Dec 1
Externally publishedYes
EventIASTED International Conference on Artificial Intelligence and Applications, AIA 2008 - Innsbruck, Austria
Duration: 2008 Feb 132008 Feb 15

Other

OtherIASTED International Conference on Artificial Intelligence and Applications, AIA 2008
CountryAustria
CityInnsbruck
Period08/2/1308/2/15

Fingerprint

Classifiers
Brain computer interface
Electroencephalography
Probability distributions
Chemical activation
Feedback

Keywords

  • Asynchronous design
  • Bayesian inference
  • Brain-computer interface
  • Idle state
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Fazli, S., Danóczy, M., Kawanabe, M., & Popescu, F. (2008). Asynchronous, adaptive BCI using movement imagination training and rest-state inference. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008 (pp. 85-90)

Asynchronous, adaptive BCI using movement imagination training and rest-state inference. / Fazli, Siamac; Danóczy, Márton; Kawanabe, Motoaki; Popescu, Florin.

Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. 2008. p. 85-90.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fazli, S, Danóczy, M, Kawanabe, M & Popescu, F 2008, Asynchronous, adaptive BCI using movement imagination training and rest-state inference. in Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. pp. 85-90, IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, Innsbruck, Austria, 08/2/13.
Fazli S, Danóczy M, Kawanabe M, Popescu F. Asynchronous, adaptive BCI using movement imagination training and rest-state inference. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. 2008. p. 85-90
Fazli, Siamac ; Danóczy, Márton ; Kawanabe, Motoaki ; Popescu, Florin. / Asynchronous, adaptive BCI using movement imagination training and rest-state inference. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. 2008. pp. 85-90
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