摘要
通过对连续长时间脑力劳动前后状态下的脑电信号进行分析,提取了脑电信号的基本尺度熵和排列熵两种复杂性测度,研究了它们与脑疲劳程度之间的关系,以及它们在不同脑疲劳状态下的变化规律及其相关性.实验结果表明,基本尺度熵和排列熵与脑疲劳程度之间存在很强的关联性,对于不同的脑疲劳状态,随着脑疲劳程度的增加,其alpha（8~12 Hz）、beta（13~30 Hz）频率段和total频率段（0.5~30 Hz）脑电信号的基本尺度熵和排列熵逐渐降低.相对于Tsallis熵算法,基本尺度熵和排列熵可以更好地反映疲劳前后脑电信号复杂度的变化特性.同时,由于基本尺度熵和排列熵算法概念简单,运算量小,因而它们的计算复杂度大大降低,运算速度更快,使得实时分析与监测脑疲劳成为可能.脑电信号的基本尺度熵和排列熵有望成为衡量脑疲劳程度的指标.
Evaluating the base-scale entropy and permutation entropy of electroencephalogram （EEG）, the relationships among these entropy parameters of EEG and mental fatigue are investigated by analyzing EEG of different mental fatigue states before and after long term mental work, and the variations of base-scale entropy and permutation entropy of EEG under different mental fatigue level are sought out. The experimental results show that the base-scale entropy and permutation entropy of EEG in total （0.5-30 Hz）, alpha （8-12 Hz） and beta （13-30 Hz） frequency band decreases with the increasing mental fatigue level. The base-scale entropy and permutation entropy of EEG are strongly correlated with the mental fatigue. Compared with Tsallis entropy, the base-scale entropy and permutation entropy can effectively reflect the complexity change of EEG before and after mental fatigue. Meanwhile, since the concepts of base-scale entropy and permutation entropy are simple and the corresponding operation task gets lighter, thus, their computational complexity is reduced largely and arithmetic speed is very fast, which makes the real-time analysis and monitor of mental fatigue possible. The base-scale entropy and permutation entropy of EEG are expected to serve as the indices for detecting mental fatigue level.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2008年第12期1555-1559,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(30670534)
关键词
脑疲劳
脑电
基本尺度熵
排列熵
mental fatigue
electroencephalogram
base-scale entropy
permutation entropy
