Model based analysis of fMRI/EEG data
Contribuinte(s) |
Nahavandi, Saeid Mohamed, Shady |
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Data(s) |
01/07/2013
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Resumo |
This thesis addresses two major topics in neuroscience literature and drawbacks from existing literature are addressed by utilising state space models and Bayesian estimation techniques. Particle filter-based joint estimation of the physiological model for time-series analysis of fMRI data is demonstrated first in the thesis and secondly the Granger causality-based effective connectivity analysis of EEG data is investigated. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Deakin University, Office of the Deputy Vice Chancellor (Research), Centre for Intelligent Systems Research |
Relação |
http://dro.deakin.edu.au/eserv/DU:30063024/hettiarachchi-agreement-2013.pdf http://dro.deakin.edu.au/eserv/DU:30063024/hettiarachchi-modelbased-2013A.pdf |
Direitos |
The Author. All Rights Reserved |
Palavras-Chave | #Neuroscience #fMRI data #EEG data #Time series analysis #Granger causality-based effective connectivity analysis |
Tipo |
Thesis |