文献详情
Exploring directed functional connectivity based on electroencephalography source signals using a global cortex factor-based multivariate autoregressive model
文献类型：期刊
期刊名称：Journal of neuroscience methods影响因子和分区
年：2019
卷：318
页码：6-16
ISSN：1872-678X
关键词：Directed functional connectivity,EEG source,Global cortex factor-based MVAR (GCF-MVAR) model,MVAR model,Weighted PDC
所属部门：心理学系
摘要：Partial directed coherence (PDC) computed from multivariate autoregressive (MVAR) model coefficient is increasingly being used to study directed functional connectivity between brain regions in the frequency domain based on electroencephalography (EEG) source signals. However, directly fitting MVAR model to the high-dimensional source signals is difficult. Besides, although PDC measurement often shows good results for simulated data, it is not clear to what extent the results for real data are p ...More
Partial directed coherence (PDC) computed from multivariate autoregressive (MVAR) model coefficient is increasingly being used to study directed functional connectivity between brain regions in the frequency domain based on electroencephalography (EEG) source signals. However, directly fitting MVAR model to the high-dimensional source signals is difficult. Besides, although PDC measurement often shows good results for simulated data, it is not clear to what extent the results for real data are physiologically plausible.We propose a new method termed global cortex factor-based MVAR (GCF-MVAR) to study directed functional connectivity based on EEG source signals. It avoids directly fitting MVAR model to high-dimensional EEG sources, instead using low-dimensional global cortex factor signals derived from the source signals by principle component analysis (PCA). To validate its physiological efficacy, we weight the PDC with source spectral power (SP) which reflects the true frequency activity in a source region.The performance of GCF-MVAR is compared with FMVAR, ROI-MVAR, and MVAR by applying to both simulated and resting-state EEG data.The simulation results show that GCF-MVAR has the lowest estimation error. By using the source SP to weight the PDC, GCF-MVAR improves the physiological interpretation of the source connectivity for both simulated and resting-state EEG data.The new method is proved to outperform than the state-of-the-art methods, and can be feasible not only for resting state studies, but also task-related connectivity and neurological disorder analysis.Copyright ? 2019 Elsevier B.V. All rights reserved. ...Hide

DOI：10.1016/j.jneumeth.2019.02.016
百度学术：Exploring directed functional connectivity based on electroencephalography source signals using a global cortex factor-based multivariate autoregressive model
语言：外文
人气指数：1
浏览次数：1
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