Fractal-Based EEG data analysis of body parts movement imagery tasks
Fractal-Based EEG data analysis of body parts movement imagery tasks
复制标题
DOI：
10.2170/physiolsci.rp006307
复制
发表时间：
2007-08-01
影响因子：
2.3
通讯作者：
Nakagawa, Masahiro
中科院分区：
文献类型：
--
作者：
Phothisonothai, Montri;Nakagawa, Masahiro
本研究的目的是分析自发脑电（EEG）数据对应的身体部位运动想象任务的分形特性。提出了分形维数估计的六种算法：计盒算法、Higuchi算法、方差分形算法、去趋势波动分析、功率谱密度分析和临界指数分析。本实验以人体运动想象的不同部位如脚、舌、食指为实验任务。记录了3名健康受试者（2名男性和1名女性）的EEG数据。实验结果是有用的，在测量FD的变化在EEG数据，并提出不同的特点，变异性。概率密度函数（PDF）也被应用到显示FD分布是沿着每个电极。本研究提出，每种方法的性能可以从想象运动的脑电数据中提取信息。
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.