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ＥＥＧ 代表了大脑神经元活动的一种电信号，它一直是人们分析和研究大脑活动和大脑功能状态的重要工具。我们利用了由Ｋｏｌｍ ｏｇｏｒｏｖ 和徐京华定义的Ｋｃ 复杂性和Ｃ１ 复杂性，对帕金森症患者和正常人的ＥＥＧ 时间序列作了分析和研究，根据４００ 例的统计结果表明，复杂性能够区分这两者，我们认为这种方法可能作为诊断帕金森症的客观指标。
EEG represents an electrical signal of neuronal activity in the brain and has long been an important tool for people to analyze and study the state of brain activity and brain function. Using the Kc complexity and C1 complexity defined by Kolmogorov and Xu Jinghua, we analyzed and studied the EEG time series of Parkinson’s disease patients and normal subjects. According to the statistical results of 400 cases, the complexity can distinguish this Both, we think this approach may serve as an objective indicator of Parkinson’s disease.