Brain computer interface approach using sensor covariance matrix with forced whitening

Hyuksoo Shin, Wonzoo Chung

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

Abstract

In this paper, we present a novel motor imagery classification method in electroencephalogmphy (EEG)-based Bmin-Computer lnterfaces (BCIs) using forced whitened sampIe covariance matdces as features. The proposed method performs a constant-forcing to the weaker sources of covadance matrices before a whitening process to prevent amplifications of noise sources which have small power relative to class relevant sources. Expedmental results show the improved accuracy in comparison with a classification without forced whitening process.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-68
Number of pages3
ISBN (Electronic)9781509050963
DOIs
Publication statusPublished - 2017 Feb 16
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 2017 Jan 92017 Jan 11

Other

Other5th International Winter Conference on Brain-Computer Interface, BCI 2017
CountryKorea, Republic of
CityGangwon Province
Period17/1/917/1/11

Keywords

  • Brain-computer interface (BCI)
  • Classification
  • Sensor covariance matrix
  • Supporting vector machine (SVM)
  • Whitening matrix

ASJC Scopus subject areas

  • Signal Processing
  • Human-Computer Interaction

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  • Cite this

    Shin, H., & Chung, W. (2017). Brain computer interface approach using sensor covariance matrix with forced whitening. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 66-68). [7858161] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858161