Dynamic Analysis And Pinning-control Of The Brain Network Model

Electroencephalogram(EEG)contains lots of physiological and pathological information of the brain.It provides a basis for the diagnosis and treatment of certain brain diseases.The brain is a complex network system.The brain network model composed of neural mass models can simulate the EEG signals with different rhythms.The dynamic analysis of the brain network model helps us find the causes of brain rhythms well and better understand the pathogenesis mechanism of brain diseases.The study of feedback control strategies for brain network models can provide new ideas for the modulation of brain rhythms and the treatment of brain diseases.In this paper,the classical neural mass model is extended to the brain network model with different topologies by using some concepts of complex network theory.The dynamic characteristics of the brain network model are analyzed statistically.And different pinning-control strategies are proposed according to different topologies of the brain network models to adjust the dynamic characteristics.Firstly,the brain network models with regular,small world,scale-free and random topologies are constructed.The modified permutation-entropy algorithm is used for the statistical analysis of the complexity of EEG signals.The influence of coupling strength,network parameter and topology on the dynamic characteristics are studied.Based on System on Programmable Chip(SOPC),the brain network model is constructed to simulate EEG signals.Secondly,the pinning-control and fuzzy PID control are applied for the control of dynamic of brain network model with small-world topology.The influence of different control strategies on the control effect is analyzed.The simulations verify the effectiveness of the specific pinning-control in brain network model with small-world connection.At the same time,the influence of the important parameters on the control energy and the number of the minimum driving nodes are analyzed.Finally,the pinning-control and fuzzy PID control are applied for the control of dynamic of brain network model with regular,small-world,scale-free and random connections.It mainly analyzes the effects of the specific pinning-control and random pinning-control in brainnetworks with different topologies,then the influence of topology of the brain network and coupling strength on the number of driving nodes are analyzed.