DWT and CNN based multi-class motor imagery electroencephalographic signal recognition

Abstract

Objective. Brain computer interface (BCI) system allows humans to control external devices through motor imagery (MI) signals. However, many existing feature extraction algorithms cannot eliminate the influence of individual differences. This research proposed a new processing algorithm that can reduce the impact of individual differences on classification and improve the universality of the algorithm. Approach. To select the optimal frequency band, the energy in each sub-band was calculated by the discrete wavelet transform. Power spectral density and visual geometric group network based convolutional neural network were used for feature extraction and classification respectively. Main results. The test of the BCI Competition IV dataset IIa proved the superiority of the algorithm. In comparison with some commonly used methods, the proposed algorithm reduced classification calculation time while improving classification accuracy; the average classification accuracy rate reaches 96.21%, which is far exceeding the results obtained by the latest literature. Significance. The good classification performance of this research was rooted in the reduced number of parameters, the reduced consumption of computing resources, and the eliminated influence of individual differences. Therefore, the proposed algorithm can be applied to a real-time multi-class BCI system.