Synchronization Problem Study Of Multi-channel Eeg Signals Model Based On The Acppunctre

Acupuncture originated in China has been used for thousands of years, as one of themost important clinical treatments. In recent decades, the influence of acupuncture in thewest is also growing, but the mechanism of acupuncture and moxibustion treatment is notclear. EEG research provides a new way for acupuncture and moxibustion theory. With thedeepening research on biological neural network system, more and more test certify that theneural network system show a very complicated and nonlinear characteristics, the researchof biological neural network system is helpful for study of nerve physiology phenomenon’smechanism, and there’s a great help for guiding the neurophysiological experiments andnerve medicine. Therefore, the research for multi-channel EEG of neural network isimportant for the theoretical significance and application value.In this paper, the main purpose is studying synchronous analysis of EEG signals by theacupuncture research. In recent years, the study of synchronization EEG problem has beenone of the most active subject in the field of neuroscience, and has been the main tool toanalysis and research various neurological diseases. The synchronization of brain electricalsignal is considered as a reflection of integration and binding on cerebral functional area bypeople. Many senior complex activities require the nervous system of specific functionalareas in the brain coordinating to complete. Within animal testing, integrating andprocessing large amounts of information in brain mainly through synchronous oscillation invarious brain neural network. Synchronization of brain electrical signal high frequencyoscillations are thought to be one of the keys to communicate between different brainregions.Because of the need of synchronous research, neurons community model is set up first,getting the wanted brain electrical signal by building a model simulate, according to theexperimental data to research synchronization phenomenon in the brain. Construct themodel of neuron community basic first, to analysis different EEG signals by changing thesignal. Then on the basis of the basic community model of neurons, extend the model to bemultiple channel coupling neuron community model, to simulate the different couplingstrength and different directions of coupling of EEG signals by changing the parameters ofmodel. Secondly, introduce single channel and dual channel synchronization analysisII methods, according to data which are made by the multi-channel coupling neuroncommunity model simulation, analyzing phase synchronization on the dual channel EEGsignals meanwhile analyzing S-estimator on multi-channel EEG signals.