【摘要】 为了反映大脑的不同生理功能和状态 ,提出一种度量脑电复杂性的新特征。采用奇异系统分析方法 ,对一维脑电信号进行延迟重构、奇异值分解和主分量分析 ,用累积贡献率 95 %所需的主分量个数作为脑电复杂性指标。对睡眠脑电的分析实验表明 ,主分量个数与脑电的复杂性正相关。这一特征能明显表达脑电的复杂性 ,进而反映大脑功能和状态更多还原

【Abstract】 In order to reflect the physiological/psychological function and state of a brain, a new complexity feature of EEG is presented. Singular system analysis is adopted in EEG time series analysis of one dimension. EEG is processed by time delay reconstruction, singular value decomposition and principal component analysis, the number of principal component needed for accumulative contribution 95% is selected as a complexity index of EEG. Experimental results by analyzing sleep EEG show that the number of the principal component is positive correlation with EEG complexity. The feature can clearly reveal the complexity of EEG, thus reflecting diffe-rent functions and states of the brain.更多还原