小波熵在脑电信号分析中的应用

小波熵是一个衡量非线性信号多尺度动力学行为有序、无序程度的量化指标,其可提供信号非线性动力学过程复杂程度的信息.近年来,小波熵在脑电信号中的研究日益受到关注,国内外学者用小波熵研究脑电信号、诱发电位、事件相关电位等的复杂程度,进一步揭示了大脑电活动的动力学机制.其主要应用于大脑感知、认知活动的研究,癫痫脑电信号的动态观测,睡眠、网络成瘾、头外伤后脑神经的康复等几个方面.小波熵不仅可以显示受到刺激后脑电信号频率上同步化的动态演变过程,而且可以有效区分癫痫发作前状态和癫痫发作状态,从而加深了对脑动力学机制的理解,成为认知功能研究的一种新的方法,显示了在脑电信号分析中良好的应用前景.

Application of wavelet entropy in EEG analysis

Wavelet entropy, as a powerful quantitative parameter to measure the ordering/disordering level of multi-scale dynamical behavior for nonlinear signals, provides information of complex degree in nonlinear dynamical process. Recently, the wavelet entropy is attracting more and more attention in electroencephalogram （EEG） signal analysis, which is employed by domestic and overseas scholars to investigate the complex degree of EEG, evoked potential and event-related potential, and to profoundly reveal the dynamic mechanism of physiological electrical activity in the brain. It is mainly used in the research of perception, cognitive activity, dynamic observation of epileptic EEG signals, sleeping, internet addiction and rehabilitation of brain after injury. Not only can the wavelet entropy represent the dynamic evolution process of the frequency synchronization for stimulated EEG signals, but also distinguish the states before and ＇after epileptic seizure, as well as to deepen the understanding of brain dynamics mechanism. The wavelet entropy is becoming a new tool for investigating cognition and exhibits a good application prospect in EEG signal analysis.