Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application

As part of a motor-imagery brain-computer interface (BCI), a deep neural network (DNN) must analyze measured electroencephalogram (EEG) data and identify neural signal patterns characteristic of a particular imagined motor movement. Our studies are intended to investigate the use of such a DNN in asynchronous online applications, where the EEG signals need to be interpreted continuously, as well as to gain insights into the learned neural patterns. We examined EEGNet, a commonly referenced convolutional neural net (CNN). In addition to the impacts of the size and temporal position of the trials used for training and testing the DNN on the classification accuracy, we examined the contributions and temporal behavior of known neural patterns and their effects on the response time of the system and the time period for which the mental state was stably recognized. Because the optimal temporal position of the trials is different for the neural patterns involved, we introduced ‘cropped training’, which is a training method in which the DNN is trained using trials with different temporal positions. This enabled the DNN to learn the neural patterns in the 0–8 Hz frequency range that are important for a short response time and the patterns in the 8–30 Hz frequency range that are important for determining the state duration. We show that cropped training is essential for achieving a good response time as well as for a good state duration.