AUTHOR=Su Rui , Li Xin , Liu Yi , Cui Wei , Xie Ping , Han Ying 

TITLE=Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 13 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2021.625081

DOI=10.3389/fnagi.2021.625081

ISSN=1663-4365

ABSTRACT=The mild cognitive impairment (MCI) stage plays an essential role in preventing conversion of older adults to Alzheimer’s disease. In this paper, Neurofeedback Training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze Electroencephalogram (EEG) features of MCI patients. To overcome the problem of traditional Multiscale Entropy (MSE) information loss, WMMSE fully considered the correlation of the sequence and each sequence’s contribution to the total entropy. The experimental group composed of 39 MCI patients was subjected to NFT for 10 days during two sessions. The control group included 21 MCI patients without any intervention.  The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of Montreal Cognitive Assessment (MoCA) results show that the post-NFT score is higher than the pre-NFT in the vast majority of the MCI patients and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function.