Classification of Motor Imagery Signal Based on Parallel Multi-scale Time Convolutional Network
The accuracy of classification methods based on deep learning is low because of the nonlinearity and low temporal spatial resolution of motor imagery signals.Therefore,a parallel multi-scale time convolutional net-work classification method was proposed to classify motor imagery signals.The proposed method extracted the time and space information of the motor imagery signal respectively,and uses parallel convolution computation method in the time dimension to extract the time features of the signal better.In addition,a simplified preprocessing method was used to simplify the complex preprocessing without affecting the accuracy of classification.Experimental results show that the classification accuracy of the proposed method on the BCI Competition IV dataset is 74.4%,and the number of parameters to be trained is reduced to 35 663.Compared with the classical classification methods such as FBCSP and DeepNet,the classification accuracy is increased by 2.2%and 6.6%,respectively,and the number of parameters is reduced by 86.4%and 87.5%,respectively,which verifies the effectiveness of the proposed method.
motor imagery signalconvolutional neural networkbrain-computer interfacesignal classifica-tionspatio-temporal characteristics
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