Fractal-Based EEG Data Analysis of Body Parts Movement Imagery Tasks

The objective of this study is to analyze the spontaneous electroencephalographic (EEG) data corresponding to body parts movement imagery tasks in terms of fractal properties. We proposed the six algorithms of fractal dimension (FD) estimators; box-counting algorithm, Higuchi algorithm, variance fra...

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Bibliographic Details
Published in:Journal of Physiological Sciences Vol. 57; no. 4; pp. 217 - 226
Main Authors: Phothisonothai, Montri, Nakagawa, Masahiro
Format: Journal Article
Language:English
Published: Japan The Physiological Society of Japan 01.01.2007
Subjects:
Adult
Algorithms
Electroencephalography - methods
Female
Fingers - physiology
Foot - physiology
Fractals
Humans
Imagination - physiology
Male
Movement - physiology
Tongue - physiology
ISSN:1880-6546
Online Access:Get full text
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Summary:The objective of this study is to analyze the spontaneous electroencephalographic (EEG) data corresponding to body parts movement imagery tasks in terms of fractal properties. We proposed the six algorithms of fractal dimension (FD) estimators; box-counting algorithm, Higuchi algorithm, variance fractal algorithm, detrended fluctuation analysis, power spectral density analysis, and critical exponent analysis. The different parts of human body movement imagination such as feet, tongue, and index finger are proposed for use as the tasks in this experiment. The EEG data were recorded from three healthy subjects (2 males and 1 female). The experimental results are useful in the measurement of FD changes in EEG data and present different characteristics in terms of variability. The probability density function (PDF) is also applied to show that the FD distribution is along each electrode. This study proposes that the performances of each method can extract information from the EEG data of imagined movement.
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ISSN:1880-6546
DOI:10.2170/physiolsci.RP006307