MI-EEG classification using Shannon complex wavelet and convolutional neural networks

Many classification methods by machine learning and convolutional neural networks (CNN) have been proposed to recognize MI-EEG recently. However, the indescribable properties and individual differences of the MI-EEG signals cause low classification accuracy. In this study, a new MI-EEG classification method was designed to improve classification accuracy by combining Shannon complex wavelets and convolutional neural networks. First, the original MI-EEG was preprocessed using EEGLAB by channel selection and bandpass filtering. Second, the Shannon complex wavelet was used as the time–frequency transform strategy to calculate the time–frequency​ matrix. Finally, an improved Resnet was used to classify the time–frequency​ matrix to complete the MI-EEG identification. BCI competition IV dataset 2b as a public motor imagination dataset was tested to prove the validation of this proposed method. The classification accuracy and kappa value were adopted to prove the superiority of the proposed method by comparing it with the state-of-the-art classification methods. Experimental results showed that the classification accuracy and kappa values are 0.852 and 0.704, respectively, and they are the highest in the state-of-the-art. The parameter influence of wavelet wavelength and interception time on classification accuracy was discussed and optimized. This method can effectively improve classification accuracy and has a wide range of applications in MI-EEG classification.