Abstract: In order to describe the fine structure and complexity of EEG components accurately, a EEG complexity method is proposed based on multiple coarse-grained EEG components. The coarse-grained thresholds of the method are selected based on the normalized statistics characters of the EEG. The complexity value is computed according to the formula of the classical LZC method. The simulation results based on the logistic map validate that the multiple LZC is much better than conventional methods and the trend of the LZC is much closer to the Lyapunov exponents. The method overcomes the defect of classical LZC complexity method, which cannot describe the fine structure of EEG characteristics effectively. Finally an assumption is proposed based on the results of the BCI IV EEG data, that is, the quadruple LZC is less than the decuple LZC when the EEG was partial periodicity and the period reached a certain threshold. The assumption is validated by the simulation data based on the EEG characters, a relationship between EEG rhythms and multiple LZC is established by which.