The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
486
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
|
Sponsor:
|
ENAR
|
Abstract - #300308 |
Title:
|
A Bayesian Spatiotemporal Model for Multi-Subject EEG
|
Author(s):
|
Wesley Thompson*+
|
Companies:
|
University of California at San Diego
|
Address:
|
9500 Gilman Drive 0664, La Jolla, CA, 92093-0664, USA
|
Keywords:
|
EEG ;
Multi-Subject ;
Bayesian ;
Functional Neuroimaging ;
Source-Localized
|
Abstract:
|
Electroencephalography (EEG) has millisecond temporal resolution but localization of neural sources inside the brain is a difficult problem. One approach is to apply independent components analysis to scalp EEG recordings to reduce the dimensionality of sources, and then to use a forward head model to determine the best-fitting source dipole location. Applied to different subjects, this typically results in varying numbers and locations of source dipoles across subjects. We develop a Bayesian spatiotemporal model for multi-subject source-localized EEG which exploits time-frequency domain relationships to obtain group inferences on the spatial locations and dynamic inter-relationships among localized sources. Each subject's localized sources is modeled as arising from a mixture distribution of spatial coordinates and time-varying partial coherence. Model inference is obtained via a Markov Chain Monte Carlo algorithm. The utility of this method is demonstrated by simulations and by application to a large dataset of event-related EEG.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.