The novel recognition method with Optimal Wavelet Packet and LSTM based Recurrent Neural Network

In order to adaptively extract the subject-based time-frequency features of motor imagery EEG (MI-EEG) and make full use of the sequential information hidden in MI-EEG features, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is integrated with Optimal Wavelet Packet Transform (OWPT) to yield a novel recognition method, denoted as OWLR. Firstly, OWPT is applied to each channel of MI-EEG, and the improved distance criterion is used to find the optimal wavelet packet subspaces, whose coefficients are further selected as the time-frequency features of MI-EEG. Finally, a LSTM based RNN is used for classifying MI-EEG features. Experiments are conducted on a publicly available dataset, and the 5-fold cross validation experimental results show that OWLR yields relatively higher classification accuracies compared to the existing approaches. This is helpful for the future research and application of RNN in processing of MI-EEG.