BCI classification using locally generated CSP features

Yongkoo Park, Wonzoo Chung

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

1 Citation (Scopus)

Abstract

In this paper, we present a novel motor imagery classification method in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) using locally generated CSP features centered at each channel. By favoring the channels with the local CSP features exhibiting significant eigenvalue disparity in the classification stage, improved performance in classification accuracy can be achieved in comparison with the conventional globally optimized CSP feature, especially for small-sample setting environments. Simulation results confirm the significant performance improvement of the proposed method for BCI competition III dataset Iva using 18 channels in the motor area.

Original languageEnglish
Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
Volume2018-January
ISBN (Electronic)9781538625743
DOIs
Publication statusPublished - 2018 Mar 9
Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
Duration: 2018 Jan 152018 Jan 17

Other

Other6th International Conference on Brain-Computer Interface, BCI 2018
CountryKorea, Republic of
CityGangWon
Period18/1/1518/1/17

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Keywords

  • Brain-Computer Interfaces (BCIs)
  • Common Spatial Pattern (CSP)
  • electroencephalography (EEG)
  • Local features

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience

Cite this

Park, Y., & Chung, W. (2018). BCI classification using locally generated CSP features. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311492