Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Optimal EEG Feature Extraction using DWT for Classification of Imagination of Hands Movement

  • Chum, Pharino (Chung-Ang University, School of Electrical and Electronics Engineering) ;
  • Park, Seung-Min (Chung-Ang University, School of Electrical and Electronics Engineering) ;
  • Ko, Kwang-Eun (Chung-Ang University, School of Electrical and Electronics Engineering) ;
  • Sim, Kwee-Bo (Chung-Ang University, School of Electrical and Electronics Engineering)
  • Received : 2011.11.19
  • Accepted : 2011.12.12
  • Published : 2011.12.25
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Abstract

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

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References

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