High accuracy classification of EEG signal

Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an...

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Main Authors: XU, Wenjie, GUAN, Cuitai, SIONG, Chng Eng, RANGANATHA, S., THULASIDAS, Manoj, WU, Jiankang
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
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Online Access:https://ink.library.smu.edu.sg/sis_research/3496
https://ink.library.smu.edu.sg/context/sis_research/article/4497/viewcontent/HighAccuracyClassificationEEGSignal_2004_ICPR.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-44972017-08-24T06:03:10Z High accuracy classification of EEG signal XU, Wenjie GUAN, Cuitai SIONG, Chng Eng RANGANATHA, S. THULASIDAS, Manoj WU, Jiankang Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%. 2004-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3496 info:doi/10.1109/ICPR.2004.1334229 https://ink.library.smu.edu.sg/context/sis_research/article/4497/viewcontent/HighAccuracyClassificationEEGSignal_2004_ICPR.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Databases and Information Systems
Graphics and Human Computer Interfaces
XU, Wenjie
GUAN, Cuitai
SIONG, Chng Eng
RANGANATHA, S.
THULASIDAS, Manoj
WU, Jiankang
High accuracy classification of EEG signal
description Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.
format text
author XU, Wenjie
GUAN, Cuitai
SIONG, Chng Eng
RANGANATHA, S.
THULASIDAS, Manoj
WU, Jiankang
author_facet XU, Wenjie
GUAN, Cuitai
SIONG, Chng Eng
RANGANATHA, S.
THULASIDAS, Manoj
WU, Jiankang
author_sort XU, Wenjie
title High accuracy classification of EEG signal
title_short High accuracy classification of EEG signal
title_full High accuracy classification of EEG signal
title_fullStr High accuracy classification of EEG signal
title_full_unstemmed High accuracy classification of EEG signal
title_sort high accuracy classification of eeg signal
publisher Institutional Knowledge at Singapore Management University
publishDate 2004
url https://ink.library.smu.edu.sg/sis_research/3496
https://ink.library.smu.edu.sg/context/sis_research/article/4497/viewcontent/HighAccuracyClassificationEEGSignal_2004_ICPR.pdf
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