[On the method of practical symbolic dynamics for EEG analysis].
Symbolic dynamics may be a new research direction for electroencephalogram (EEG) signal analysis. Symbolic entropy can reflect the degree of complexity of nonlinear signal simply and reliably. In this paper, we propose a new method to symbolize the EEG signal, namely difference symbolization, by which we can analyze the characteristics of dynamics on the tangent space of observation data. Furthermore, we compare and analyze the value of symbolic entropy by choosing difference symbolization parameter. The comparative analyses on different physiological state of EEG data sets show that this method can clearly distinguish the EEGs' complexity between normal and epilepsy, between eyes open and eyes closed, and so on. And it is of significance to the establishment of objective criteria for evaluation and fine quantitative analysis of EEG.
Liu
Hanlin
H
School of Electrical Engineering and Information, Sichuan University, Chengdu 610064, China.
Huang
Hua
H
Liu
Kuangbin
K
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted