Brain-Computer Interfaced Robot: Classification of Motor Imagery Tasks in BCI Robotics Control
Lafayette College
File access restricted to members of the Lafayette College community.
Description
Applications of brain-computer interface (BCI) systems have grown in importance for assist- ing individuals with severe motor disabilities in the navigation of our increasingly techno- logically dependent society. With applications including 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 to send back to a PC via Bluetooth for further processing. Signal-processing algorithms and models were developed and implemented in order to determine the user’s mental activity and send signals to the external physical device. While the implementation with the robot has yet to be completed, simulation data demonstrates promising results for future real-time asynchronous control of physical devices.
Title
Brain-Computer Interfaced Robot: Classification of Motor Imagery Tasks in BCI Robotics Control
Digital collection of student honors theses, beginning in academic year 2021-2022.
Past theses written by Lafayette students through academic year 2020-2021 are kept in Special Collections and College Archives. Information about the honors theses in Special Collections is available in the Library Catalog.