Abstract
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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61371193).
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Zhang, XY., Wang, WR., Shen, CY., Sun, Y., Huang, LX. (2018). Extraction of EEG Components Based on Time - Frequency Blind Source Separation. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-63859-1_1
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DOI: https://doi.org/10.1007/978-3-319-63859-1_1
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