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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 1742862, 25 pages
https://doi.org/10.1155/2017/1742862
Review Article

Progress in EEG-Based Brain Robot Interaction Systems

1School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
2Department of Computer & Electrical Engineering and Computer Science, California State University, Bakersfield, CA 93311, USA
3State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, Liaoning 110016, China
4Department of Math and Computer Science, West Virginia State University, Institute, WV 25112, USA
5Intelligent Fusion Technology, Inc., Germantown, MD 20876, USA

Correspondence should be addressed to Wei Li; ude.busc@ilw

Received 28 December 2016; Accepted 21 March 2017; Published 5 April 2017

Academic Editor: Hasan Ayaz

Copyright © 2017 Xiaoqian Mao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.