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

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 neurohea...

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Vydáno v:Multimedia tools and applications Ročník 75; číslo 13; s. 7999 - 8017
Hlavní autoři: Li, Yantao, Zhou, Gang, Graham, Daniel, Holtzhauer, Andrew
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2016
Springer Nature B.V
Témata:
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
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Popis
Shrnutí: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-015-2717-z