Control of a Robotic Arm Using Low-Dimensional EMG and ECoG Biofeedback

Abstract

In this dissertation we describe a system that uses a low dimensional input derived from electromyography and electrocorticography data to control a robot. The work involves creating a system that allows signals recorded directly from a human body to allow control of a small robot arm. We compare direct joystick control with electromyogram (EMG) input to determine if one input system is superior, or if the quality of control between them is comparable. We also verify the system that is used to record the electromyogram signals is adaptable to other forms of biosignal input; in particular, direct connection to a human brain via electrocorticography (ECoG). Because of the current limitations in sensing and interpreting biological signals, the dimensionality of the data available through these signals is low. Our system is designed to use these low dimensional data and map specific patterns to resulting actions of a robot arm.