Robust analysis of dynamical change in scalp electroencephalogram time series

近年来，我们对大脑功能的理解有了令人兴奋的发展。然而，许多问题仍未得到解决。其中包括对大脑的动态分析，这是科学界最大的挑战之一。这种分析有助于理解、预测和识别大脑功能。头皮脑电图（EEG）时间序列是一种反映大脑活动生理状态的无创记录。分析EEG时间序列的动态变化在许多研究和诊断应用中非常有用，例如检测不同的睡眠阶段和病理状态，包括癫痫发作、精神疾病、阿尔茨海默病、帕金森病和克雅氏病。

In recent years, there have been exciting developments in our understanding of brain functions. However, many problems remain unsolved. Among them is dynamical analysis of the brain that is one of the biggest challenges in science. This analysis helps in understanding, predicting and recognizing brain functions. Scalp electroencephalogram (EEG) time series is a non-invasive recording that reflects physiological states of brain activities. Analysis of dynamical change in EEG time series can be extremely useful in many research and diagnosis applications, such as detecting different sleep stages and pathologic states including epileptic seizure, psychiatric disorders, Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases.