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

Chu Kiong Loo,Gin Chong Lee,Andrews Samraj

doi:10.1155/2011/724697

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.

Highlights

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands, providing nonmuscular interaction with the environment
Katz’s method was the fastest method, and combining it with FKNN, the highest classification accuracy of 85% the three electrode configuration and k 9 as well as the second highest classification accuracy of 83% the two electrode configuration and k 9 were achieved
R/S method with any classifier performed the slowest with the classification accuracies and the computation times ranging from 69% to 71% and 7.32 to 11.07 s, respectively

Summary

Introduction

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands, providing nonmuscular interaction with the environment. Movement preparation, and motor imagery desynchronize SMRs, whereas during relaxation or postmovement, they are synchronized 1. Since motor imagery does not require any muscular activity, motor imagery-regulated SMRs are commonly utilized in BCI 2, 3. This is beneficial for people with neurological disorders, since their voluntary muscular activities might be impaired. Another advantage of the utilization of motor imagery-regulated SMRs in BCI is the short training period required 4. Motor imagery tasks are identified by detecting the synchronization and desynchronization of SMRs. The most common motor imagery tasks are imagery hand 5 , foot 4 , and tongue 3 movements. SMRs are analyzed using preprocessing, feature extraction, and classification operations

Methods

Results

Conclusion