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Type of Document Master's Thesis Author Yang, Manshu Author's Email Address myang@nd.edu URN etd-10292008-224657 Title Using State-Space Model with Regime Switching to Represent the Dynamics of Facial EMG Data Degree Master of Arts Department Psychology Advisory Committee
Advisor Name Title Scott E. Maxwell Committee Chair Gitta Lubke Committee Member Guangjian Zhang Committee Member Sy-Miin Chow Committee Member Keywords
- facial EMG
- dynamical system
- time series analysis
- regime-switching model
Date of Defense 2008-07-11 Availability restricted Abstract Facial electromyography (EMG) is a useful physiological measure for detectingsubtle affective changes in real time. It can differentiate valence of emotion, capture
transient and covert affective response, and provide an approximate continuous-time
measure of emotion change. In this thesis, facial EMG data is analyzed using
time series analysis methods. By allowing certain parameters to switch between
several discrete stages (regimes), regime-switching models can be used to describe
heterogeneous transition patterns and capture time-varying association between
EMG signals and other covariates. The main purpose of this thesis is to construct
and propose different regime-switching state-space models suited for representing the
time-varying dynamics of facial EMG data and its relationship with self-reported
affect intensity. The Kim filter, which is an extension of the Kalman filter, is
proposed to estimate latent states and the Gaussian maximum likelihood method
is used for parameter estimation. Indices for diagnostic checks and model fit
evaluations will be discussed for model comparison purposes. Results based on
empirical EMG data indicate that regime-switching model with autoregressive
regression slope (the RS-AR model) is most appropriate to represent the EMG
dynamics. Monte Carlo simulation results also indicate that parameters in the
proposed model can be accurately recovered under different conditions.
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