Robot control based on EMG correlates of facial expressions using a BCI system

Electromyography-based control applications became popular in recent days since they can be implemented using cheap brain-computer interface devices. In the past, for classifying electromyogram data, human experts would extract features from raw signals manually. Unfortunately, this is a time-consuming process and requires extensive domain knowledge. With the advancement of artificial intelligence, such procedures can be done automatically using deep learning architectures. In this study, we designed a deep convolutional network to classify electromyogram signals. Furthermore, a support vector machine-based classifier was also implemented as a reference system. The goal of this study was to develop a control architecture, in which the user interface is equipped with a cheap commercial brain-computer interface system for mobile robot control. We collected a considerable amount of electromyogram signals during different facial expressions that can be used as intervention signals. Finally, we implemented an online real-time control method based on the deep convolutional network. The system enables the user to drive a mobile robot platform solely with electromyogram signals. Manipulating the designed system, after a short initial practice period, the users could navigate through a predefined path with a low error rate.