Mental tasks classifications using S-transform for BCI applications
Proceeding of The Conference on Sustainable Utilization Development in Engineering and Technology (STUDENT 2011) at Semenyih, Malaysia on 20 October 2011 through 21 October 2011. Link to publisher's homepage http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1
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2014
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my.unimap-343762014-05-09T01:51:01Z Mental tasks classifications using S-transform for BCI applications Vijean, Vikneswaran Hariharan, Muthusamy, Dr. Saidatul, A. Sazali, Yaacob, Prof. Dr. vicky.86max@gmail.com hari@unimap.edu.my s.yaacob@unimap.edu.my Electroencephalogram K-means nearest neighborhood Linear discriminant analysis Proceeding of The Conference on Sustainable Utilization Development in Engineering and Technology (STUDENT 2011) at Semenyih, Malaysia on 20 October 2011 through 21 October 2011. Link to publisher's homepage http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1 The classification of different types of mental tasks is an active area of research that seems to be ever expanding. This field is gaining interest from researchers all over the world. This study is intended to utilize the Stockwell transform (ST) to investigate the classification accuracy of five different types of mental tasks. A well known electroencephalogram (EEG) database (Keirn and Aunon database) has been used in this study. Two subjects from the database were considered for the study. k-means nearest neighborhood (k-NN) and Linear Discriminant Analysis (LDA) based classifiers were used to perform a pair-wise classification of the 10 combinations of mental tasks. Two different discriminant functions such as linear and quadratic were used in LDA classifier and their effects on the classification performance are presented. The effect of different k values (1 to 10) was also studied in kNN algorithm. Conventional and k-fold cross validation methods were used to investigate the reliability of the classification results of the classifiers. The experimental results show that the proposed method gives promising pair-wise classification accuracy from 78.80% to 100%. 2014-05-09T01:45:54Z 2014-05-09T01:45:54Z 2011-10 Working Paper p. 69-73 978-1-4577-0443-7 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6089327 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34376 http://dx.doi.org/10.1109/STUDENT.2011.6089327 en Proceeding of The Conference on Sustainable Utilization Development in Engineering and Technology (STUDENT 2011); IEEE Conference Publications |
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Electroencephalogram K-means nearest neighborhood Linear discriminant analysis |
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Electroencephalogram K-means nearest neighborhood Linear discriminant analysis Vijean, Vikneswaran Hariharan, Muthusamy, Dr. Saidatul, A. Sazali, Yaacob, Prof. Dr. Mental tasks classifications using S-transform for BCI applications |
description |
Proceeding of The Conference on Sustainable Utilization Development in Engineering and Technology (STUDENT 2011) at Semenyih, Malaysia on 20 October 2011 through 21 October 2011.
Link to publisher's homepage http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp?tag=1 |
author2 |
vicky.86max@gmail.com |
author_facet |
vicky.86max@gmail.com Vijean, Vikneswaran Hariharan, Muthusamy, Dr. Saidatul, A. Sazali, Yaacob, Prof. Dr. |
format |
Working Paper |
author |
Vijean, Vikneswaran Hariharan, Muthusamy, Dr. Saidatul, A. Sazali, Yaacob, Prof. Dr. |
author_sort |
Vijean, Vikneswaran |
title |
Mental tasks classifications using S-transform for BCI applications |
title_short |
Mental tasks classifications using S-transform for BCI applications |
title_full |
Mental tasks classifications using S-transform for BCI applications |
title_fullStr |
Mental tasks classifications using S-transform for BCI applications |
title_full_unstemmed |
Mental tasks classifications using S-transform for BCI applications |
title_sort |
mental tasks classifications using s-transform for bci applications |
publisher |
IEEE Conference Publications |
publishDate |
2014 |
url |
http://dspace.unimap.edu.my:80/dspace/handle/123456789/34376 |
_version_ |
1643797462202187776 |
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13.18916 |