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

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,

Preview

PDF Evaluation_of_Methods_for_Estimating.pdf Download (493kB)

Abstract

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.

Item Type:	Article
Funders:	UNSPECIFIED
Uncontrolled Keywords:	Brain activity; communication; computer science; artificial intelligence; fractal dimension estimation
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User:	Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited:	19 Mar 2013 00:33
Last Modified:	19 Mar 2013 00:33
URI:	http://eprints.um.edu.my/id/eprint/5182

Actions (login required)

View Item