EEG Source Imaging Based on fMRI Functional Network and Bayesian Matrix Decomposition

LIU Ke; YANG Dong; DENG Xin

doi:10.11999/JEIT210764

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3447-3457

LIU Ke, YANG Dong, DENG Xin. EEG Source Imaging Based on fMRI Functional Network and Bayesian Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3447-3457. doi: 10.11999/JEIT210764

Citation:

LIU Ke, YANG Dong, DENG Xin. EEG Source Imaging Based on fMRI Functional Network and Bayesian Matrix Decomposition[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3447-3457. doi: 10.11999/JEIT210764

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EEG Source Imaging Based on fMRI Functional Network and Bayesian Matrix Decomposition

doi: 10.11999/JEIT210764

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61703065), Chongqing Research Program of Application Foundation and Advanced Technology (cstc2018jcyjAX0151), The Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0284), The Scientific and Technological Research Program of Chongqing (CQ) Municipal Education Commission (KJQN202000625)

Received Date: 2021-08-02
Accepted Date: 2022-01-12
Rev Recd Date: 2022-01-07

Available Online: 2022-02-18

Publish Date: 2022-10-19

Abstract

Abstract

ElectroEncephaloGraphy (EEG) is an important brain functional imaging technology. The task to reconstruct cortical activities based on the scalp EEG is called EEG source imaging. However, the accurate reconstruction of the locations and sizes of brain source activity remains a challenge. To employ fully the spatiotemporal complementary information of EEG and functional Magnetic Resonance Imaging (fMRI), a new EEG source imaging algorithm, i.e., FN-STCSI (Functional Network based Spatio-Temporal Constrains Source Imaging) is proposed. Specifically, to make full use of the temporal information of EEG signals, the source signal matrix is decomposed into a linear combination of several time basis functions based on the idea of matrix decomposition. Additionally, to fuse the high spatial resolution information of fMRI, FN-STCSI employes independent component analysis to extract the fMRI functional networks. Then these fMRI networks are used to construct the spatial covariance basis for EEG source imaging. Variational Bayesian inference techniques are used to determine the relative contribution of each spatial covariance basis to realize EEG-fMRI fusion. Through Monte Carlo numerical simulation and experimental data analysis, FN-STCSI is compared with existing algorithms under different signal-to-noise ratios and different prior conditions. The results show that FN-STCSI can effectively fuse the complementary spatiotemporal information of EEG-fMRI and improve the performance of EEG extended source imaging.

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References(27)

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