Research And Application On Chaotic Characters Of Electroencephalograph

Electroencephalograph(EEG) expresses complex electrophysiological procedure of human beings, which contains many information related to physiological structure and state. Because of the complexity of EEG, the background mechanism of EEG oscillation is still unknown. EEG is regarded as a kind of non-stationary signal. Brain’s activity may not be reflected by using traditional signal processing method. However, the nonlinear dynamic method, such as chaos and bifurcation, becomes a new way to study EEG.At present, some results have been obtained by using nonlinear dynamical method, but much more puzzles are necessary to be considered so that the brain be-havior can be understood better. Nonlinear characters of EEG model under different conditions are researched in this thesis. Considering some factors, such as disease, senescence, may cause lag of brain’s feedback during information processing, time delay is introduced into a new neural network model. The result shows that time delay will cause Hopf bifurcation and oscillation. A derivative feedback controller is designed, which can eliminate Hopf bifurcation phenomenon. This result provides theoretical explaination for clinical and experiment research. Furthermore, a class of EEG model is researched, which includes two coupled neuron populations, one is ex-citatory population, and the other is inhibitory population. The stability conditions are achieved by reduction and linearization. The complexity of EEG is researched by adjusting the excitatory input. By bifurcation diagram and Lyapunov exponent diagram, bifurcation, periodic windows and chaos are shown when the model under-goes different input parameters. Considering stimulation in visual evoked potential, a class of cortical column model is also researched. By adjusting the connections between neurons and pyramidal cells, the model shows several kinds of EEG char-acters. With certain stimulation, the model undergoes Hopf bifurcation. Moreover, the relationship between stability and periodic oscillations of the model is discussed.The brain-computer interface(BCI) is a novel kind of human computer interface and recently it is an active topic in brain function research. BCI technology can help improve the quality of life and restore function for people with severe motor disabil- ities. In order to apply chaotic character to prosthetic hand control system, EEG experiment is designed to study nonlinear characters by changing visual information input. By using chaotic time series analysis method, the correlation dimension is analyzed with or without visual input. The result shows that brain remains self-regulation when visual input is blocked, and EEG complexity is lower. The EEG complexity becomes high due to visual information input. Furthermore, a new EEG dimension complexity method is proposed. Linear range of correlation integral is obtained. By dividing original data into several groups, this new method decreases the overload of calculation. The effect on precision is also discussed. Numerical simulation verifies the effectiveness of this new method.Finally, based on the theory and experiment above, an EEG-based prosthetic hand control system is designed. The hardware platform is developed, which includes amplifier, filter, photoelectricity isolation, digital-analog converter etc. An analysis software is programmed. Based on chaotic characters of EEG, prosthetic hand is controlled sucessfully, and high accuracy is achieved.