Investigation of methods for extracting features related to motor imagery and resting states in EEG-based BCI system

I. Putu Susila, Shinichiro Kanoh, Ko Ichiro Miyamoto, Tatsuo Yoshinobu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Methods for extracting features of motor imagery from 1-channel bipolar EEG were evaluated. The EEG power spectrums which were used as feature vectors were calculated with filter bank, FFT and AR model, and were then classified by linear discriminant analysis (LDA) to discriminate motor imagery and resting states. It was shown that the extraction method using AR model gave the best result with the average true positive rate of 83% (a -7%). Furthermore, when principal component analysis (PCA) was applied to the feature vectors, the dimension of the feature vectors could be reduced without decreasing accuracy of discrimination.

Original languageEnglish
Pages (from-to)1828-1833
Number of pages6
JournalIEEJ Transactions on Electronics, Information and Systems
Volume129
Issue number10
DOIs
Publication statusPublished - 2009
Externally publishedYes

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Keywords

  • Brain-computer interface (BCI)
  • Feature extraction
  • Linear discriminant analysis (LDA), principal component analysis (PCA)
  • Motor imagery

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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