Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data

Wei Yen Hsu, Chao Hung Lin, Hsien Jen Hsu, Po Hsun Chen, I. Ru Chen

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.

Original languageEnglish
Pages (from-to)2743-2749
Number of pages7
JournalExpert Systems with Applications
Volume39
Issue number3
DOIs
Publication statusPublished - Feb 15 2012
Externally publishedYes

Keywords

  • Amplitude modulation (AM)
  • Brain-computer interface (BCI)
  • Discrete wavelet transform (DWT)
  • Electroencephalogram (EEG)
  • Independent component analysis (ICA)
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Fingerprint Dive into the research topics of 'Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data'. Together they form a unique fingerprint.

  • Cite this