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Implementing eigen features methods/neural network for EEG signal analysis

Proceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013) at Coimbatore, Tamilnadu, India on 4 January 2013 through 5 January 2013.

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Bibliographic Details
Main Authors: Saidatul Ardeenawatie, Awang, Pandiyan, Paulraj Murugesa, Prof. Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: saidatul@unimap.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2014
Subjects:
EEG signal
Modified Covariance
MUSIC
Neural Network
Pisarenko
Power Spectral Density
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34157
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spelling my.unimap-341572014-04-29T04:40:36Z Implementing eigen features methods/neural network for EEG signal analysis Saidatul Ardeenawatie, Awang Pandiyan, Paulraj Murugesa, Prof. Dr. Sazali, Yaacob, Prof. Dr. saidatul@unimap.edu.my paul@unimap.edu.my s.yaacob@unimap.edu.my EEG signal Modified Covariance MUSIC Neural Network Pisarenko Power Spectral Density Proceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013) at Coimbatore, Tamilnadu, India on 4 January 2013 through 5 January 2013. This paper presented the possibility of implementing eigenvector methods to represent the features of electroencephalogram signal. In this study, three eigenvector methods were investigated namely Pisarenko, Multiple Signal Classification (MUSIC) and Modified Covariance. The ability of the features in representing good character of signal in order to discriminate two different EEG signals for relaxation and writing signal were tested using neural network. The power level obtained by eigenvector methods of the EEG signals were used as inputs of the neural network trained with Levenberg-Marquardt algorithm. The classification result shows that Modified Covariance method is a better technique to extract features for relaxation-writing task. 2014-04-29T04:40:36Z 2014-04-29T04:40:36Z 2013-01 Working Paper p. 201-204 978-146734601-6 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6481149 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34157 en Proceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013); Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic EEG signal
Modified Covariance
MUSIC
Neural Network
Pisarenko
Power Spectral Density
spellingShingle EEG signal
Modified Covariance
MUSIC
Neural Network
Pisarenko
Power Spectral Density
Saidatul Ardeenawatie, Awang
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Implementing eigen features methods/neural network for EEG signal analysis
description Proceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013) at Coimbatore, Tamilnadu, India on 4 January 2013 through 5 January 2013.
author2 saidatul@unimap.edu.my
author_facet saidatul@unimap.edu.my
Saidatul Ardeenawatie, Awang
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
format Working Paper
author Saidatul Ardeenawatie, Awang
Pandiyan, Paulraj Murugesa, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Saidatul Ardeenawatie, Awang
title Implementing eigen features methods/neural network for EEG signal analysis
title_short Implementing eigen features methods/neural network for EEG signal analysis
title_full Implementing eigen features methods/neural network for EEG signal analysis
title_fullStr Implementing eigen features methods/neural network for EEG signal analysis
title_full_unstemmed Implementing eigen features methods/neural network for EEG signal analysis
title_sort implementing eigen features methods/neural network for eeg signal analysis
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34157
_version_ 1643797411099836416
score 13.214268

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