Towards an EEG-based brain-computer interface for online robot control

• Published in: Multimedia tools and applications Vol. 75; no. 13; pp. 7999 - 8017
• Main Authors: Li, Yantao, Zhou, Gang, Graham, Daniel, Holtzhauer, Andrew
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2016; Springer Nature B.V
• Subjects: Adaptive technology; Algorithms; Amyotrophic lateral sclerosis; Analysis; Classification; Clustering; Commands; Computer Communication Networks; Computer Science; Control algorithms; Data collection; Data Structures and Information Theory; Distance education; Distance learning; Electroencephalography; Feedback; Headsets; Human-computer interface; Hypotheses; Multimedia Information Systems; Paralysis; Patients; Principal components analysis; Robots; Sensors; Special Purpose and Application-Based Systems; Studies; User interface; Wheelchairs; clustering algorithm; -; EEG; BCI system; Principal component analysis
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-015-2717-z

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k - means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision , recall , and the F -measure, and our results show that the algorithms can accurately classify the subjects’ thoughts into corresponding action commands.