Research On Eeg Characteristics And Neurofeedback Regulation Effect Of Amnestic Mild Cognitive Impairment

Mild Cognitive Impairment(MCI)is an intermediate stage between cognitive normal and dementia that is a critical period for the diagnosis and treatment of Alzheimer’s Disease(AD).Amnestic MCI(aMCI)is not entirely equivalent to MCI due to AD,but it will likely progress to AD.aMCI is a widely recognized subtype of MCI.The electrophysiological characteristics of the electroencephalogram(EEG)can effectively reflect the state of cognitive function.Analyzing and mining meaningful electroencephalographic features of aMCI to assist in diagnosing and regulating aMCI is a significant task.This paper provided a comprehensive quantitative indicator for evaluating cognitive function in aMCI using nonlinear dynamic and coupled features of brain functional networks.The main research work is as follows:(1)The comprehensive quantitative index for the nonlinear dynamics of EEG was constructed to explore the nonlinear dynamic characteristics of aMCI.A new Weighted Multiple Multiscale Sample Entropy(WMMSE)algorithm was proposed to effectively quantify the complexity of EEG signals.The WMMSE solves the information loss problem caused by traditional multiscale entropy shortening the length of the original time series.Multiple parameter overcomes the problem of information loss.The weight parameter reduces fluctuations between different scales.The WMMSE,fuzzy entropy,and clinical cognitive assessment scales were used to construct a comprehensive quantitative index for the nonlinear dynamics of aMCI.(2)A new method for constructing the Genuine Symbolic Nonlinear Granger Causality(GSNGC)brain functional network was proposed to analyze the functional connectivity and coupling characteristics of aMCI.To solve the problem of connectivity and causal driving in the aMCI brain function network,the GSNGC method was implemented.The GSNGC effectively quantified the differences in brain functional network connectivity strength and graph theory characteristics between aMCI and normal control(NC)and verified the existence of information exchange in aMCI brain functional network.Finally,the GSNGC combined with the consistency analysis of the clinical cognitive assessment scale to achieve aMCI cognitive functional connectivity and coupling analysis.(3)A high-order aMCI brain functional network was constructed based on the research of low-order brain functional networks.Further considering the impact of time dimension on brain functional network connectivity,a dynamic high-low order brain functional network was constructed.Based on this multidimensional brain functional network model,the functional connectivity and coupling characteristics of cognitive function changes in aMCI were analyzed,as well as the changes in the integration and separation states of aMCI brain functional network in the temporal dimension.(4)The regulation effect of neurofeedback training(NFT)on the cognitive function of aMCI was explored using the comprehensive quantitative index.The comprehensive quantitative index was constructed by proposing nonlinear dynamics,multidimensional brain function network methods,and the clinical cognitive assessment scale.This study assists in the auxiliary neural regulation of aMCI.This study constructed an EEG feature evaluation scheme for aMCI patients using nonlinear dynamics,brain functional network features,and a clinical cognitive assessment scale.A systematic analysis of the cognitive function and NFT intervention effectiveness of aMCI was completed through the evaluation scheme,providing a new method for diagnosing and assisting intervening in aMCI.