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Brain Signal AnalysisAdvances in Neuroelectric and Neuromagnetic Methods$
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Todd C. Handy

Print publication date: 2009

Print ISBN-13: 9780262013086

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262013086.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 19 September 2021

Dynamic Causal Modeling for Evoked Responses

Dynamic Causal Modeling for Evoked Responses

Chapter:
(p.141) 6 Dynamic Causal Modeling for Evoked Responses
Source:
Brain Signal Analysis
Author(s):

Stefan J. Kiebel

Marta I. Garrido

Karl J. Friston

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262013086.003.0006

This chapter describes the dynamic causal modeling (DCM) equations, demonstrates how the ensuing model is inverted using Bayesian techniques, and reports the use of Bayesian priors to derive better magnetoencephalography/electoencephalography (EEG) models. It discusses the current DCM algorithms and some promising future developments, and explores the EEG data acquired under a mismatch negativity paradigm. The three plausible models defined under a given architecture and dynamics are examined. The chapter shows that evoked responses, due to bilateral sensory input (e.g., visually or auditory), could be analyzed using DCMs with symmetry priors.

Keywords:   dynamic causal modeling, Bayesian techniques, magnetoencephalography, electoencephalography, mismatch negativity, evoked responses, bilateral sensory input

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