Investigation of a Deep Learning based Brain-Computer Interface with Respect to a Continuous Control Application

In this paper we report on our investigations on the use of a Deep Learning based brain-computer interface in the context of a continuous control application. A continuous control application is an application where a Deep neural network (DNN) is supposed to recognize defined motor-imagery related neural states from continuously measured EEG data stream and to initiate corresponding actions. Decisive for the quality of such an application is the achieved response time of the system, which means the time it takes to detect a certain motor-imagery induced neural state, as well as the state detection time, which means the time duration for which this neural state is recognized. Our investigations show that the neural patterns in the 0-8 Hz low-frequency band are essential for a short response time and that the patterns valid in the 8-30 Hz frequency band should be used to achieve a good state detection time. We show that both parameters, response time and state detection time, can be significantly improved when so-called cropped training method is used to train the deep neural net. Reaching a short response time and a good state detection time is significant for most continuous control applications. To the best of our knowledge, this is the first time the use of cropped training to optimize a continuous control application has been investigated.