Improving source detection and separation in a spatiotemporal Bayesian inference dipole analysis.

Abstract:

:Most existing spatiotemporal multi-dipole approaches for MEG/EEG source localization assume that the dipoles are active for the full time range being analysed. If the actual time range of activity of sources is significantly shorter than the time range being analysed, the detectability, localization and time-course determination of such sources may be adversely affected, especially for weak sources. In order to improve detectability and reconstruction of such sources, it is natural to add active time range information (starting time point and ending time point of source activation) for each candidate source as unknown parameters in the analysis. However, this adds additional nonlinear free parameters that could burden the analysis and could be unfeasible for some methods. Recently, we described a spatiotemporal Bayesian inference multi-dipole analysis for the MEG/EEG inverse problem. This approach treated the number of dipoles as a free parameter, produced realistic uncertainty estimates using a Markov chain Monte Carlo numerical sampling of the posterior distribution and included a method to reduce the unwanted effects of local minima. In this paper, our spatiotemporal Bayesian inference multi-dipole analysis is extended to incorporate active time range parameters of starting and stopping time points. The properties of this analysis in comparison to the previous one without active time range parameters are demonstrated through extensive studies using both simulated and empirical MEG data.

journal_name

Phys Med Biol

authors

Jun SC,George JS,Plis SM,Ranken DM,Schmidt DM,Wood CC

doi

10.1088/0031-9155/51/10/004

subject

Has Abstract

pub_date

2006-05-21 00:00:00

pages

2395-414

issue

10

eissn

0031-9155

issn

1361-6560

pii

S0031-9155(06)07961-9

journal_volume

51

pub_type

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