High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton. Conference Paper uri icon

Overview

abstract

  • 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.

publication date

  • January 1, 2013

Research

keywords

  • Brain-Computer Interfaces
  • Electroencephalography
  • Intention
  • Paraplegia
  • Robotics

Identity

PubMed Central ID

  • PMC3801445

Scopus Document Identifier

  • 84886508188

Digital Object Identifier (DOI)

  • 10.1109/EMBC.2013.6610821

PubMed ID

  • 24111008

Additional Document Info

volume

  • 2013