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A Bayesian hierarchical model for EMG data

Dr. Quentin Gronau
University of Newcastle
Sauro Salomoni
University of Tasmania
Prof. Mark Hinder
University of Tasmania ~ Psychological Sciences
Niek Stevenson
University of Amsterdam ~ Brain & Cognition
Dr. Dora Matzke
University of Amsterdam ~ Psychological Methods
Andrew Heathcote
University of Tasmania ~ Division of Psychology

Recently, cognitive modelers have become increasingly interested in supplementing behavioral data with neural or physiological measures. In order to complement approaches that use a generative cognitive model of behavioral choice data, we develop a generative model of modulations in the variance of the electromyographical (EMG) recordings associated with pressing one or two response buttons. This model provides estimates of key quantities of interest such as onset, offset, and amplitude of EMG bursts for each response. The hierarchical structure (i.e., trials nested within participants) yields group-level estimates for these parameters for each participant. We use particle Metropolis within Gibbs sampling (Gunawan et al., 2020) to efficiently obtain posterior samples from the model. The model can be used to address questions of interest about the EMG signal itself (such as between-condition differences) but also holds the promise of linking EMG parameters to cognitive model parameters in a joint model.


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Cite this as:

Gronau, Q. F., Salomoni, S. E., Hinder, M. R., Stevenson, N., Matzke, D., & Heathcote, A. (2022, July). A Bayesian hierarchical model for EMG data. Paper presented at Virtual MathPsych/ICCM 2022. Via