基于平滑最小方差无失真响应的一致性同步算法研究

doi:10.3969/j.issn.0258-8021.2018.06.006

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Abstract EEG coherence reflects the degree of spectral correlation between two-channels EEG signals, which can assess the connections between neurons in the brain. Combined the coherence method based on minimum variance distortionless response (MVDR) with kernel filter, a new method named smoothing minimum variance distortionless response (SMVDR) was proposed in this study. The simulation analysis indicated that the new method SMVDR provides more accuracy and better anti-noise performance in both narrow-band signals and broad-band signals. SMVDR was used to analyze the EEG coherence of 18 amnesic MCI (aMCI) and 13 normal controls of patients with diabetes between different brain regions in four frequency bands (delta, theta, alpha, beta). We observed a decrease in delta coherence and an increase in beta coherence in left temporal-right temporal region, and an increase in theta coherence in frontal-occipital region, and an increase in alpha coherence in both right temporal-occipital region and frontal-right temporal region in aMCI patients through statistical analysis. Correlation analysis between coherence values and MOCA scores shows that coherence values in alpha band and delta band and MOCA scores had a significant positive correlation in some specific channels, while coherence values in both theta band and beta band and MOCA scores had a significant negative correlation. The new method SMVDR can compute the coherence better between two-channels EEG signals. It is important in exploring the mechanism, early diagnosis and intervention treatment of aMCI.

Key words： EEG mild cognitive impairment coherence smoothing minimum variance distortionless response

Received: 26 August 2017

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R318

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	Articles by authors
	Gu Guanghua
	Cui Dong
	Wang Juan
	Qi Shunai
	Li Xiaoli

Cite this article:

Gu Guanghua,Cui Dong,Wang Juan, et al. Coherence Synchronization Analysis Based on Smoothing Minimum Variance Distortionless Response[J]. Chinese Journal of Biomedical Engineering, 2018, 37(6): 680-687.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.06.006 OR http://cjbme.csbme.org/EN/Y2018/V37/I6/680

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