New Complexity Feature of EEG
Yuan Yu · Shuju caiji yu chuli · 2005
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.