Motor imagery brain-computer interface (BCI) by using of deep-learning models is proposed in this paper. In which, we used the electroencephalogram (EEG) signals of motor imagery (MI-EEG) to identify different imagery activities. The brain dynamics of motor imagery are usually measured by EEG as non-stationary time series of low signal-to-noise ratio. However, a variety of methods have been previously developed to classify MI-EEG signals getting not satisfactory results owing to lack of characteristics in time-frequency features. In this paper, discrete wavelet transform (DWT) was applied to transform MIEEG signals and extract their effective coefficients as the time-frequency features. Then two deep learning (DL) models named Long-short term memory (LSTM) and gated recurrent neural networks (GRNN) are used to classify MI-EEG data. LSTM is designed to fight against vanishing gradients. GRNN makes each recurrent unit to capture dependencies of different time scales adaptively. Similar scheme of the LSTM unit, GRNN has gating units that modulate the flow of information inside the unit, but without having a separate memory cells. Experimental results show that GRNN and LSTM yield higher classification accuracies compared to the existing approaches that is helpful for the further research and application of relative RNN in processing of MI-EEG.
Part of the book: Evolving BCI Therapy