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Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface

Shang-ming Zhou Orcid Logo, John Q. Gan, Francisco Sepulveda

Information Sciences, Volume: 178, Issue: 6, Pages: 1629 - 1640

Swansea University Author: Shang-ming Zhou Orcid Logo

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DOI (Published version): 10.1016/j.ins.2007.11.012

Abstract

This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems...

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Published in: Information Sciences
ISSN: 00200255
Published: INFORMATION SCIENCES 2008
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URI: https://cronfa.swan.ac.uk/Record/cronfa10025
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first_indexed 2013-07-23T12:02:38Z
last_indexed 2019-07-17T13:49:51Z
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spelling 2019-07-17T12:03:40.3154651 v2 10025 2012-03-21 Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2012-03-21 BMS This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the brain-computer interface (BCI) 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate. Journal Article Information Sciences 178 6 1629 1640 INFORMATION SCIENCES 00200255 Brain–computer interfaces, Classification, Electroencephalogram (EEG), Feature extraction, Higher-order statistics, Bispectrum 15 3 2008 2008-03-15 10.1016/j.ins.2007.11.012 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-07-17T12:03:40.3154651 2012-03-21T16:17:09.0000000 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-ming Zhou 0000-0002-0719-9353 1 John Q. Gan 2 Francisco Sepulveda 3
title Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
spellingShingle Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
Shang-ming Zhou
title_short Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
title_full Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
title_fullStr Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
title_full_unstemmed Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
title_sort Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
author Shang-ming Zhou
author2 Shang-ming Zhou
John Q. Gan
Francisco Sepulveda
format Journal article
container_title Information Sciences
container_volume 178
container_issue 6
container_start_page 1629
publishDate 2008
institution Swansea University
issn 00200255
doi_str_mv 10.1016/j.ins.2007.11.012
publisher INFORMATION SCIENCES
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 0
active_str 0
description This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the brain-computer interface (BCI) 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.
published_date 2008-03-15T03:10:38Z
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score 11.012678

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