Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

Yongkoo Park, Wonzoo Chung

Research output: Contribution to journalArticle

Abstract

In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods.

Original languageEnglish
Article number9119424
Pages (from-to)111514-111521
Number of pages8
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • brain-computer interfaces (BCIs)
  • channel selection
  • common spatial pattern (CSP)
  • correlation coefficient
  • Electroencephalography (EEG)

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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