Research Of The EEG Grey Processing Methods

Electroencephalogram (EEG) is electrical phenomenon represented from pallium or scalp surface of the electrical activities about brain nerve cell. In general, EEG could be divided into two types, evoked potentials and spontaneous EEG. Many researches show that EEG is characteristic of stronger background noise and fainter signals amplitude, higher non-steady and randomicity, relatively prominence of frequency character, et al. Therefore, the analysis and processing of the EEG is still a very challenge subject.Grey System Theory (GST) is created by Chinese scholar professor Deng Julong in 1982. For non classic regularity signals such as non-steady, non-Gaussian and non-white noise, GST has the obvious advantages than other processing methods according to statistical and transcendent regularities.In this paper, in the basis of analyzing the processing methods of the EEG and GST, meanwhile, considering that higher non-steady and randomicity, obvious frequency character, and that for the modeling data grey modeling has not specific demands, a novel method is put forward for the analysis and processing of the EEG signals, i.e. the theory of grey modeling is used into the feature extraction of the Spontaneous EEG At the same time, using the EEG features from grey modeling approach, the classification of the recognized EEG signals are performed by the k -Nearest Neighbor (k-NN) Algorithm from instance-based learning method (IBL). The results of the research show that in the processing of the EEG signals, it is applicable and available by using grey modeling method to perform feature extraction and making classification according to k-NN Algorithm. Meanwhile, this method also provides well theory foundation for further research of the pattern recognition of the EEG. Specifically, in this paper, the following work is performed:(1) Modeling GM (1, 1) for EEG signals.(2) Parameters estimation of the model and EEG feature extraction.(3) Feature parameters a, b for two states (eyes-open and eyes-closed) are analyzed and compared and the comparing results are given.(4) The classification of unknown EEG patterns (eyes-open and eyes-closed) is performed by using k -NN algorithm from instance-based learning methods and the classification performance of the algorithm under the different parameters and improving methods are presented.The whole analysis work is completed under the MATLAB environment. The eventual results of the research show that the method proposed in the paper is applicable and available.