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Design and implementation of a brain-computer interface based on virtual instrumentation

Design and implementation of a brain-computer interface based on virtual instrumentation The paper presents an on-line brain-computer interface (BCI) based on visual evoked potential (VEP) P300. The BCI is applied to control a multi-DOF manipulator. This BCI system includes five modules which are visual stimulator, signal acquisition, data processing, communication and motion control of the manipulator. In the experiment, the subject chooses the right oddball on a CRT/LCD displayer with eight blocks which are corresponding to the actions of the manipulator and gazes at it. The electroencephalography (EEG) of the subject is sampled to extract P300 feature. The algorithms of peak extraction, correlation analysis and wavelet transform are used to analyse EEG. The manipulator is controlled to move or operate by the subject's EEG with wire or wireless communication. The experiments show that the subject with little training can control the manipulator. The application and the future improvement of the research are also available in the paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Advanced Mechatronic Systems Inderscience Publishers

Design and implementation of a brain-computer interface based on virtual instrumentation

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-8412
eISSN
1756-8420
DOI
10.1504/IJAMechS.2010.030847
Publisher site
See Article on Publisher Site

Abstract

The paper presents an on-line brain-computer interface (BCI) based on visual evoked potential (VEP) P300. The BCI is applied to control a multi-DOF manipulator. This BCI system includes five modules which are visual stimulator, signal acquisition, data processing, communication and motion control of the manipulator. In the experiment, the subject chooses the right oddball on a CRT/LCD displayer with eight blocks which are corresponding to the actions of the manipulator and gazes at it. The electroencephalography (EEG) of the subject is sampled to extract P300 feature. The algorithms of peak extraction, correlation analysis and wavelet transform are used to analyse EEG. The manipulator is controlled to move or operate by the subject's EEG with wire or wireless communication. The experiments show that the subject with little training can control the manipulator. The application and the future improvement of the research are also available in the paper.

Journal

International Journal of Advanced Mechatronic SystemsInderscience Publishers

Published: Jan 1, 2010

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