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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2011/724697 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850159122483249152 |
|---|---|
| author | Chu Kiong Loo Andrews Samraj Gin Chong Lee |
| author_facet | Chu Kiong Loo Andrews Samraj Gin Chong Lee |
| author_sort | Chu Kiong Loo |
| collection | DOAJ |
| 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 |
| id | doaj-art-5f9555432a5f4292a5f14609cce0b05b |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5f9555432a5f4292a5f14609cce0b05b2025-08-20T02:23:40ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2011-01-01201110.1155/2011/724697724697Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer InterfaceChu Kiong Loo0Andrews Samraj1Gin Chong Lee2Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, MalaysiaSchool of Computing Science and Engineering, VIT University, Chennai Campus, Vandalor-Kellambakkam Road, Chennai 48, IndiaFaculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Malacca, MalaysiaA 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.http://dx.doi.org/10.1155/2011/724697 |
| spellingShingle | Chu Kiong Loo Andrews Samraj Gin Chong Lee Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface Discrete Dynamics in Nature and Society |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2011/724697 |
| work_keys_str_mv | AT chukiongloo evaluationofmethodsforestimatingfractaldimensioninmotorimagerybasedbraincomputerinterface AT andrewssamraj evaluationofmethodsforestimatingfractaldimensioninmotorimagerybasedbraincomputerinterface AT ginchonglee evaluationofmethodsforestimatingfractaldimensioninmotorimagerybasedbraincomputerinterface |