A Hybrid BCI for Robotic Device Navigation.

Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics in mind, the goal of this research is to develop a system that enables the use of electroencephalographic (EEG) and electromyographic (EMG) signals to control the movement of a robot. An EEG cap was used to obtain occipital alpha power density, frontal muscular artifacts, and sensorimotor mu rhythms, which were then sent back to a PC via Bluetooth for further processing. Signal-processing algorithms and models were developed and implemented to determine the user’s mental activity and send signals to the external physical device. The preliminary results from the pilot experiments were very promising. The algorithms will be implemented for real-time signal processing and tested with a BCI-controlled robotic device.