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A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image

Motor imagery classification using electroencephalogram (EEG) is increasingly becoming popular in brain-computer interface (BCI) field. In this paper, we present a novel convolution neural networks (CNN) approach for classifying motor imagery electroencephalogram (EEG). For this, we obtained time-frequency representations of EEGs in cases of left and right motor imageries using continuous wavelet transform (CWT). Through experiments using well known public benchmark dataset, it shows that the proposed method provides the improved accuracy compared to the traditional machine learning based classification methods. It confirms the usefulness of the CNN scheme for BCI research field.