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Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification acc...

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Main Authors: Loo, C.K., Samraj, A., Lee, G.C.
Format: Article
Language:English
Published: 2011
Subjects:
T Technology (General)
Online Access:http://eprints.um.edu.my/5182/1/Evaluation_of_Methods_for_Estimating.pdf
http://eprints.um.edu.my/5182/
http://www.hindawi.com/journals/ddns/2011/724697/
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spelling my.um.eprints.51822013-03-19T00:33:16Z http://eprints.um.edu.my/5182/ Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface Loo, C.K. Samraj, A. Lee, G.C. T Technology (General) A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85, and further improvements by 3 were achieved by implementing the TDFD method. 2011 Article PeerReviewed application/pdf en http://eprints.um.edu.my/5182/1/Evaluation_of_Methods_for_Estimating.pdf Loo, C.K. and Samraj, A. and Lee, G.C. (2011) Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface. Discrete Dynamics in Nature and Society, 2011. ISSN 1026-0226 http://www.hindawi.com/journals/ddns/2011/724697/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Loo, C.K.
Samraj, A.
Lee, G.C.
Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
description A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85, and further improvements by 3 were achieved by implementing the TDFD method.
format Article
author Loo, C.K.
Samraj, A.
Lee, G.C.
author_facet Loo, C.K.
Samraj, A.
Lee, G.C.
author_sort Loo, C.K.
title Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
title_short Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
title_full Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
title_fullStr Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
title_full_unstemmed Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
title_sort evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface
publishDate 2011
url http://eprints.um.edu.my/5182/1/Evaluation_of_Methods_for_Estimating.pdf
http://eprints.um.edu.my/5182/
http://www.hindawi.com/journals/ddns/2011/724697/
_version_ 1643687507503611904
score 13.211869

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