, Volume 75, Issue 4, pp 744-771

Using State-Space Model with Regime Switching to Represent the Dynamics of Facial Electromyography (EMG) Data

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Abstract

Facial electromyography (EMG) is a useful physiological measure for detecting subtle affective changes in real time. A time series of EMG data contains bursts of electrical activity that increase in magnitude when the pertinent facial muscles are activated. Whereas previous methods for detecting EMG activation are often based on deterministic or externally imposed thresholds, we used regime-switching models to probabilistically classify each individual’s time series into latent “regimes” characterized by similar error variance and dynamic patterns. We also allowed the association between EMG signals and self-reported affect ratings to vary between regimes and found that the relationship between these two markers did in fact vary over time. The potential utility of using regime-switching models to detect activation patterns in EMG data and to summarize the temporal characteristics of EMG activities is discussed.

Preparation of this article was supported in part by the National Science Foundation grant BCS-0826844 awarded to Sy-Miin Chow.