| Brain is a very complex non-linear system, and electroencephalograph (EEG) and electrocorticogram (ECoG) are also non-stationary and nonlinear in nature, signal processing methods based on linear and stationary assumptions, although has obtained important result, can not grasp the entire picture of neuroscience. Complexity and time-frequency analysis give us chances to analyze EEG by these entirely new methods, in the first part of this paper coarse graining of Lempel-Ziv complexity was analyzed and in the second part Hilbert-Huang transform was used to analyze motor imagery.Lempel-Ziv complexity is a useful method to analyze EEG, but current binary processing might lose some useful information of the original signal. In addition, complexity is different at different scales, and we can derive some useful information by calculating complexity at small scale, so sometimes we need to calculate Lempel-Ziv complexity at different scale. Increasing the number of symbol can decrease the information loss but need more data and time for calculation. In this paper we divide the original signal into more than two parts, "0" and "1" denote decrease and increase respectively. This new binary method can reduce the information loss and calculate Lempel-Ziv complexity at multi-scale but do not increase the number of symbol. The following works testify the validity of this new binary method:We test this method with Logistic map added by sine wave and have a good result when the added sine wave has low frequency; This new approach was also presented for distinguishing textual images from pictorial images and we found that the pictorial images' complexity had more significant increase than the textual images' complexity did when the calculating scale became small. The test shows that multi-scale Lempel-Ziv complexity can be used as an image classification method or as a feature of image classifier; Schizophrenia and normal EEG was classified bylarge-scale compleiity and small-seale complexity, the error rate of small-scale complexity is low than large-scale complexity.Motor imagery is a branch of brain computer interface (BCI). As EEG is non-stationary. Hilbert-Huang Transform (HHT), a novel approach for analyzing nonlinear and non-stationary signals, was presented to motor imagery analysis. The basis function of HHT comes from signal itself, the decomposition is local, self-adaptive and data driven. The results are as follows:When BCI2005 data Illb (EEG) was analyzed we observed event-related desynchronization (ERD) and event-related synchronization (ERS) at different frequency. Using standard deviation of amplitude and energy in time-frequency window as a feature to classify imagined left and right hand movement, the error of one subject is lower than 10%.One subject of data Illb has asymmetry phenomena, that is, only one of the two symmetry leads was used to classify two imagined movements, one can get error rate as low as 27.43% while the other get error as high as 37.69%.We use the same feature as used in data Illb to classify imagined tongue and left small finger movement (ECoG BCI2005 data I), the lowest error rate is 16.19% by featuring from one time-frequency window. Using standard deviation of time-frequency window from 7 electrodes, single frequency band and 11 continuous time windows as feature, the error rate can be as low as 11.87%. Compared with energy, the standard deviation of time-frequency window performs better. Surface Laplacian Filter is a good EEG preprocessing method, we use this method to preprocess ECoG but can not reduce error rate, this prompt us that not all method used in EEG can be used to analyze ECoG.
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