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Discrete Dynamics in Nature and Society
Volume 2011, Article ID 724697, 8 pages
Research Article

Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

1Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
2School of Computing Science and Engineering, VIT University, Chennai Campus, Vandalor-Kellambakkam Road, Chennai 48, India
3Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Malacca, Malaysia

Received 12 July 2011; Accepted 19 October 2011

Academic Editor: Xiaohui Liu

Copyright © 2011 Chu Kiong Loo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.