Please use this identifier to cite or link to this item:
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34157
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saidatul Ardeenawatie, Awang | - |
dc.contributor.author | Pandiyan, Paulraj Murugesa, Prof. Dr. | - |
dc.contributor.author | Sazali, Yaacob, Prof. Dr. | - |
dc.date.accessioned | 2014-04-29T04:40:36Z | - |
dc.date.available | 2014-04-29T04:40:36Z | - |
dc.date.issued | 2013-01 | - |
dc.identifier.citation | p. 201-204 | en_US |
dc.identifier.isbn | 978-146734601-6 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6481149 | - |
dc.identifier.uri | http://dspace.unimap.edu.my:80/dspace/handle/123456789/34157 | - |
dc.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. | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartofseries | Proceeding of 7th International Conference on Intelligent Systems and Control 2013 (ISCO 2013); | - |
dc.subject | EEG signal | en_US |
dc.subject | Modified Covariance | en_US |
dc.subject | MUSIC | en_US |
dc.subject | Neural Network | en_US |
dc.subject | Pisarenko | en_US |
dc.subject | Power Spectral Density | en_US |
dc.title | Implementing eigen features methods/neural network for EEG signal analysis | en_US |
dc.type | Working Paper | en_US |
dc.contributor.url | saidatul@unimap.edu.my | en_US |
dc.contributor.url | paul@unimap.edu.my | en_US |
dc.contributor.url | s.yaacob@unimap.edu.my | en_US |
Appears in Collections: | Conference Papers Sazali Yaacob, Prof. Dr. Paulraj Murugesa Pandiyan, Assoc. Prof. Dr. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Implementing eigen features methods-neural network for EEG signal analysis.pdf | Access is limited to UniMAP community | 456.11 kB | Adobe PDF | View/Open |
Items in UniMAP Library Digital Repository are protected by copyright, with all rights reserved, unless otherwise indicated.