Dynamic Analysis Of Visual Response Signal Based On Brain Network

Steady-state visual evoked potentials(SSVEP)have the advantages of fixed frequency,strong robustness,and high signal-to-noise ratio,and have been widely used in braincomputer interface,neural engineering,clinical medicine and other fields.Current research shows that SSVEP is mainly produced in cortical areas such as occipital lobe and frontal lobe and has a certain degree of frequency sensitivity.However,due to the harsh physiological experimental conditions and the lag in the level of brain network modeling,related research is still mainly focused on the local circuit level.The lack of in-depth research at the whole brain level hinders the large-scale application of the SSVEP-BCI system to a certain extent.Therefore,in this thesis,an in-depth study of the dynamic characteristics of SSVEP response signals at the whole brain level is carried out.The specific contents are as follows:(1)This thesis constructs a whole-brain network model containing 76 local brain regions based on brain structural connectivity data.First,the Reduced Wong-Wang model was used to characterize the dynamic behavior of local brain regions,and the low short-range correlation of local circuits was ensured by dynamically adjusting feedback-inhibitory synaptic coupling parameters.Then,the local dynamic behavior of each brain region is integrated by structural connection and global dynamic parameters,and the global coupling factor is explored to make the network reach the optimal working point,thereby establishing a whole-brain network model.Finally,the validity of the model was verified by analyzing the amplitude change and power difference of the SSVEP signal induced by stimulation.The results showed that the primary visual cortex(V1)and secondary visual cortex(V2)were stimulated to produce SSVEP responses,and the power values of the SSVEP signals in the V1 and V2 regions at the stimulation frequency were significantly enhanced.,which indicates that the model can effectively reflect the generation mechanism of SSVEP response,which lays a foundation for in-depth study of the dynamic characteristics of SSVEP response.(2)In this thesis,based on the constructed whole-brain network model,we deeply study the frequency sensitivity characteristics of SSVEP and its causes.Firstly,the effects of the amplitude and frequency of periodic stimulation on the power of SSVEP were studied.The results showed that stimulation amplitude had little effect on SSVEP power.Under different frequency stimulation,the SSVEP power value in V1 and other regions has the same trend as the resting state,and the SSVEP power value is significantly higher in the low frequency(2-8HZ),which further proves that the frequency sensitivity of SSVEP is caused by the internal oscillation of the brain.It is triggered by entrainment and resonance caused by the synergy of external periodic stimuli.Then,a functional brain network was constructed by calculating the coherence of brain regions to analyze the characteristic changes of the brain network induced by stimulation.The results show that the coherence of visual-related brain regions is significantly enhanced under periodic stimulation,and the network has stronger clustering coefficient,global efficiency,local efficiency and shorter characteristic path length under low frequency(2-8HZ)stimulation.higher information transfer efficiency.Finally,the network synchronization factor was calculated to measure the synchronization of the brain network under different frequency stimulation.The results show that the network has stronger synchronization under low frequency stimulation,which indicates that the main factor for the improvement of the information transfer efficiency of the evoked brain network is the enhancement of network synchronization.In summary,in order to explore the dynamic characteristics of SSVEP response at the wholebrain level,this thesis constructs a whole-brain network model constrained by structural connectivity data,and conducts simulation experiments on this basis.SSVEP has been studied in depth in aspects such as synchronicity.