Extraction of EEG Components Based on Time - Frequency Blind Source Separation

  • Xue-Ying ZhangEmail author
  • Wei-Rong Wang
  • Cheng-Ye Shen
  • Ying Sun
  • Li-Xia Huang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 82)


In order to extract EEG characteristic waves better, this paper adopts the method of combining wavelet transform with time-frequency blind source separation based on smooth pseudo Wigner-Ville distribution. Firstly, the EEG signal is extracted by wavelet transform to reconstruct the β wave band signal and reconstructed as the initial extracted characteristic wave. Then, to remove the other components which are less relevant to get the enhanced beta wave signal, the time-frequency blind source separation technique based on the smooth pseudo-Wigner distribution is used for the initial extracted Target wave. Finally, the features are extracted, and the support vector machine is used to classify and identify the emotional categories. The experimental results show that the recognition rate is improved when the characteristic wave is extracted by using wavelet transform only.


EEG Smoothed pseudo Wigner-Ville distribution Emotion recognition β Wavelet transform Blind source separation 



This work is supported by the National Natural Science Foundation of China (No. 61371193).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Xue-Ying Zhang
    • 1
    Email author
  • Wei-Rong Wang
    • 1
  • Cheng-Ye Shen
    • 1
  • Ying Sun
    • 1
  • Li-Xia Huang
    • 1
  1. 1.College of Information EngineeringTaiyuan University of TechnologyTaiyuanChina

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