A2FWPO: Anti-aliasing filter based on whale parameter optimization method for feature extraction and recognition of dance motor imagery EEG

Abstract

The classification accuracy of EEG signals based on traditional machine learning methods is low. Therefore, this paper proposes a new model for the feature extraction and recognition of dance motor imagery EEG, which makes full use of the advantage of anti-aliasing filter based on whale parameter optimization method. The anti-aliasing filter is used for preprocessing, and the filtered signal is extracted by two-dimensional empirical wavelet transform. The extracted feature is input to the robust support matrix machine to complete pattern recognition. In pattern recognition process, an improved whale algorithm is used to dynamically adjust the optimal parameters of individual subjects. Experiments are carried out on two public data sets to verify that anti-aliasing filter-based preprocessing can improve signal feature discrimination. The improved whale algorithm can find the optimal parameters of robust support matrix machine classification for individuals. This presented method can improve the recognition rate of dance motion image. Compared with other advanced methods, the proposed method requires less samples and computing resources, and it is suitable for the practical application of brain-computer interface.