Continuous EEG signal analysis for asynchronous BCI application

Wei Yen Hsu

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)


In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

Original languageEnglish
Pages (from-to)335-350
Number of pages16
JournalInternational Journal of Neural Systems
Issue number4
Publication statusPublished - Aug 2011
Externally publishedYes


  • Asynchronous brain-computer interface (BCI)
  • electroencephalogram (EEG)
  • fractal dimension
  • independent component analysis (ICA)
  • support vector machine (SVM)
  • wavelet transform

ASJC Scopus subject areas

  • Computer Networks and Communications


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