High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton

Brain-Machine Interface (BMI) systems allow users to control external mechanical systems using their thoughts. Commonly used in literature are invasive techniques to acquire brain signals and decode user's attempted motions to drive these systems (e.g. a robotic manipulator). In this work we use a lower-body exoskeleton and measure the users brain activity using non-invasive electroencephalography (EEG). The main focus of this study is to decode a paraplegic subject's motion intentions and provide him with the ability of walking with a lower-body exoskeleton accordingly. We present our novel method of decoding with high offline evaluation accuracies (around 98%), our closed loop implementation structure with considerably short on-site training time (around 38 sec), and preliminary results from the real-time closed loop implementation (NeuroRex) with a paraplegic test subject.

I INTRODUCTION

Having the potential of increasing the quality of life for paraplegic and tetraplegic population, BMI systems to control lower-body and upper-body exoskeletons became a focus of research for the past decade. Researchers recently reported the control of external physical devices (robotic manipulators, prostheses) and computer cursors using invasive methods [1], [2], [3]. Some major limitations of invasive methods are the risks associated with surgery and degradation in signal quality over time. Non-invasive methods typically acquire brain signals using scalp electroencephalography (EEG). Although having a small signal-to noise ratio compared to the intracortical methods, recent results show the possibility of using non-invasive (risk-free) decoding of delta-band brain activity using EEG to predict the human limb movements to reliably drive a BMI [4], [5], [6]. We have also reported the feasibility of a single session training followed by a successful on-line decoding [7] of EEG signals.