Brain-Mobility-Interface based on Deep Learning Techniques for Classifying EEG Signals into Control Commands

This paper proposes an interface that enables users to mentally control a personal mobility robot, PMR. The user interface is named as brain-mobility-interface, BMI. In the BMI, EEG signals of a user are measured and fed as inputs. From the EEG signals, the brain state and face direction of the user, indicating the intention for PMR control, are estimated. For this purpose, two control command classifiers based on deep neural networks, DNNs, are applied to the BMI. As the output, the EEG signals are classified into control commands depending on the estimated user's intentions. The control commands are composed of linear and angular velocities of the PMR. Through the network training, the estimation performance of both the classifiers is increased to more than 99 [%]. In the control experiment, furthermore, we show that the classification performance of the BMI is enough for a user to control the PMR as intended with only the mental commands.