Research On Nonlinear Of The Electroencephalogram Of Epileptic

Analyzing the Electroencephalogram and studying the relationship between the state of brain function and it which is an important cognitive and thinking tool of many researchers in the field of Electroencephalogram. The new development of nonlinear dynamics has created a new way for the study of EEG The difficulty of modeling the brain as a continuous and self-organizing non-linear complicated system and looking on the EEG as the low dimension chaos signals come from the its inner non-linearity. In the chaos phenomenon of this non-linear system, the certainty of the determinative system motion will permit further understanding of the overall characteristics of dynamics ,although the long term estimation of the time-sequences. It is feasible to study the EEG in the non-linear perspective because studying the micro happening of the inner neural activity will not be a must which can permit us to forecast the activity of nerve in the angle of the whole system.This paper is devoted to the understanding the state space reconstruction and correlative dimension and the calculation of L!, in addition to the understanding of the relationship between the embedding dimension of the State space reconstruction and delay time. Based on the high-dimension chaos characteristics of EEG, using the analysis in the multi-variable state space reconstruction, better estimation of the correlative dimension can be obtained by splitting the brain into two parts and using the 8 L2 as the reconstruction samples.To test the feasibility of the algorithms , low-dimension Lorenz system was firstly tested at the request the the amount of data, then the test on L3was done. After Comparing the high-dimension data of the EEG of normal people with the results of other researchers , it is indicated that the multivariable State space reconstruction is applicable in short time noise-containing time sequences to obtain more reliable results and can free the researchers from the choice of delay time and the embedding dimension .