Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm

Abstract

Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.

Keywords: BGSA; Channel selection; Motor imagery; PPWPE.

Fig. 1

PPWPE calculation process. The figure a is the process of calculating WPE for class 1, the figure b is the calculation process of PPWPE for all channels

Fig. 2

Experiment procedure. The figure a is the timing of single trail of Data set 1. The figure b is the timing of single trail of Data set 2

Fig. 3

PPWPE + CSP channel selection algorithm. All channels were sorted by PPWPE value, selected half of channels with larger PPWPE value. According to the order of the sorting results, the channels were added one by one until all the selected channels were added. Feature were extracted by CSP algorithm, and fed into SVM classifier. Determine the final channel combination based on the classification accuracy obtained

Fig. 4

PPWPE + BGSA + CSP channel selection algorithm. First, based on the calculated PPWPE value of each channel, half of the channels with larger PPWPE value are selected. Then, used the remaining channels as search space and performed iterative search using BGSA algorithm. Determined the final channel selection scheme based on the obtained classification accuracy

Fig. 5

Number of selected channels in Data set 1 and Data set 2. Mean (1) and Mean (2) represent the average number of selected channels in Data set 1 and Data set 2

Fig. 6

Accuracy with respect to different number of features for 5 subjects from Data set 2

Fig. 7

The mean classification accuracy of the two data sets varies with the number of channels. All channels were sorted by PPWPE value, according to the sorting result, increased the channel one by one

Fig. 8

Distribution of channels selected using PPWPE + BGSA + CSP

Fig. 9

Comparison of feature distribution (Data set 1, subject ‘a’ and ‘f’; Data set 2, subject ‘av’ and ‘aw’). Two subplots in the column show the results of each subject with different methods (All-C + CSP, PPWPE + BGSA + CSP). The horizontal and vertical axes represent two features extracted in each trail. The feature distribution with PPWPE + BGSA + CSP become more discriminative

Fig. 10

Average accuracy for ten agents in every iteration for all subjects from two data sets