Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (9): 1245-1251.DOI: 10.12068/j.issn.1005-3026.2019.09.006

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Channel Automatic Selection Algorithm for P300 Signal with Group Sparsity Bayesian Logistic Regression

FENG Bao1,2, ZHANG Shao-rong2   

  1. 1. School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510640, China; 2. Department of Automation, Guilin University of Aerospace Technology, Guilin 541004, China.
  • Received:2018-07-25 Revised:2018-07-25 Online:2019-09-15 Published:2019-09-17
  • Contact: FENG Bao
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Abstract: In order to improve the classification accuracy and calculation speed of P300 electroencephalogram(EEG)signals in the brain-computer interface(BCI), a channel automatic selection algorithm of P300 EEG signal based on group sparsity Bayesian logistic regression was proposed. First, the algorithm established the decoding model of P300 EEG signals under the Bayesian framework, and then, a priori group automatic relevance determination(GARD)was proposed to determine the weight coefficients of P300 EEG channels under group sparse constraints. Finally, the maximum likelihood estimation was used to solve the hyperparameters and select the optimal subset of P300 EEG channels, avoiding a large number of cross-validation processes. The proposed method was verified on the BCI competition dataset and self-acquisition dataset. The experimental results showed that the proposed method can automatically select P300 related channels and may improve the accuracy of P300 feature classification.

Key words: automatic relevance determination(ARD), group sparsity Bayesian, channel selection, P300, brain-computer interface(BCI)

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