|
Activity Number:
|
87
- Invited ePoster Session: a Statistical Smörgåsbord
|
|
Type:
|
Invited
|
|
Date/Time:
|
Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
|
Abstract #330765
|
|
|
Title:
|
The Analysis of Face Perception MEG and EEG Data Using a Potts-Mixture Spatiotemporal Joint Model
|
|
Author(s):
|
Yin Song* and Farouk Nathoo and Arif Babul
|
|
Companies:
|
University of Victoria and and University of Victoria
|
|
Keywords:
|
Bayesian Mixture Model;
Electromagnetic Inverse Problem;
Iterated Conditional Modes;
Maxwell's Equations;
Potts Model;
Spatiotemporal Model
|
|
Abstract:
|
In this paper we analyze magnetoencephalography (MEG) and electroencephalography (EEG) data from a single subject with the objective of determining the location and dynamics of brain activity when this subject is repeatedly presented with stimuli corresponding to pictures of scrambled faces and required to make a symmetry judgement. We propose a new Bayesian finite mixture state-space model that builds on previously developed models and incorporates two major extensions that are required for our application: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed meso-states. We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. The proposed method results in a novel estimator for the number of mixture components and is able to select active brain regions.
|
Authors who are presenting talks have a * after their name.
