Research article summary (published 27 Feb 2009):

A classification method of different motor imagery tasks based on fractal features for brain-machine interface.

Full Abstract

The objective of this study is to classify spontaneous electroencephalogram (EEG) signal on the basis of fractal concepts. Four motor imagery tasks (left hand movement, right hand movement, feet movement, and tongue movement) were investigated for each EEG recording session. Ten subjects volunteered to participate in this study. As we known, fractal geometry is a mathematical tool for dealing with complex systems like EEG signal. Therefore, we used the fractal dimension (FD) as feature for the application of brain-machine interface (BMI). Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded FD values between relaxing and imaging states of the recorded EEG signal. To show the pattern of FDs, we propose a windowing-based method or also called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. The K-L divergence and different expected values are employed as the input parameters of classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results show that the proposed method is more effective than the conventional methods.

Author information

Author/s: Phothisonothai, Montri (M); Nakagawa, Masahiro (M);

Affiliation: Chaos and Fractals Information Processing Laboratory, Department of Electrical Engineering, Burapha University, 169 Bangsaen, Chonburi 20131, Thailand. montrip(-atsign-)gmail.com

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of integrative neuroscience (J Integr Neurosci), published in England. (Language: eng)

Reference: 2009-Mar; vol 8 (issue 1) : pp 95-122

Dates: Created 2009/05/04; Completed 2009/07/29;

PMID: 19412982, status: MEDLINE (last retrieval date: 8/20/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Brain - physiology Brain Mapping Electroencephalography Fractals Humans Imagery (Psychotherapy) - methods

Motor Activity - physiology Neural Networks (Computer) Signal Processing, Computer-Assisted Task Performance and Analysis Time Factors User-Computer Interface

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