Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 94-99.doi: 10.13190/j.jbupt.2020-208

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An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network

LI Duan-ling1, CHENG Li-wei1, YU Gong-jing2, ZHANG Zhong-hai2, YU Shu-yue2   

  1. 1. School of Modern Post(School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. Beijing Aerospace Measurement & Control Technology Company Limited, Beijing 100041, China
  • Received:2020-10-12 Online:2021-06-28 Published:2021-06-23

Abstract: Aiming at the classification accuracy of the motor imagery electroencephalogram in processing is low, a processing method based on the combination of energy (second-order moment) wavelet packet transform and Levenberg-Marquardt neural network is proposed. Firstly, the energy method is used to analyze signal in the time domain, and the effective time sequence is selected. Then, wavelet packet transform is used to decompose the time-frequency of each pilot signal in the selected effective time-domain segment, and the frequency information related to the imagination task is selected to reconstruct the signal characteristics. Finally, the features reconstructed by each guide signal are concatenated and imported into the neural network based on the Levenberg-Marquardt training algorithm to realize the task classification. The method was verified by two kinds of electroencephalogram signal standard competition database, and the classification accuracy is 95.62% and 90.13%, respectively. Compared with some recent research results, this algorithm has a better processing effect.

Key words: motor imagery electroencephalogram, second-order moment, wavelet packet transformation, back propagation neural network, Levenberg-Marquardt algorithm

CLC Number: