Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct 30:2020:8837954.
doi: 10.1155/2020/8837954. eCollection 2020.

A Novel Bayesian Approach for EEG Source Localization

Vangelis P Oikonomou  1 , Ioannis Kompatsiaris  1
Affiliations

A Novel Bayesian Approach for EEG Source Localization

Vangelis P Oikonomou et al. Comput Intell Neurosci. .

Abstract

We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Obtained performance measures in the case of cortical-based lead field matrix.
Figure 2
Figure 2
Obtained performance measures in the case of volumetric-based lead field matrix.
Figure 3
Figure 3
Brain maps showing EEG sources in the case of auditory EEG data. (a) Fan. (b) Fan smooth. (c) MNE. (d) RVM-VB. (e) Champ.
Figure 4
Figure 4
Brain maps showing EEG sources in the case of Visual (Faces) EEG data. (a) Fan. (b) Fan smooth. (c) MNE. (d) RVM-VB. (e) Champ.
Figure 5
Figure 5
Brain maps showing EEG sources in the case of SSVEP EEG data. (a) Fan. (b) Fan smooth. (c) MNE. (d) RVM-VB. (e) Champ.
Figure 6
Figure 6
Brain maps (projected on the cortical surface) showing EEG sources in the case of Visual (Faces) EEG data when a volumetric-based lead field matrix is used. (a) Fan. (b) FanGr. (c) Champ.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Liu S., Cao R., Huang Y., Ouypornkochagorn T., Ji J. Time sequence learning for electrical impedance tomography using bayesian spatiotemporal priors. IEEE Transactions on Instrumentation and Measurement. 2020;69(9):6045–6057. doi: 10.1109/tim.2020.2972172. - DOI
    1. Liu S., Huang Y., Wu H., Tan C., Jia J. Efficient multi-task structure-aware sparse bayesian learning for frequency-difference electrical impedance tomography. IEEE Transactions on Industrial Informatics. 2020;20:p. 1.
    1. He B., Yang L., Wilke C., Yuan H. Electrophysiological imaging of brain activity and connectivity—challenges and opportunities. IEEE Transactions on Biomedical Engineering. 2011;58:1918–1931. - PMC - PubMed
    1. Sekihara K., Nagarajan S. S. Electromagnetic Brain Imaging - A Bayesian Perspective. Berlin, Germany: Springer; 2015.
    1. Grech R., Cassar T., Muscat J., et al. Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation. 2008;5 doi: 10.1186/1743-0003-5-25. - DOI - PMC - PubMed

LinkOut - more resources