Peripheral physiological signals have shown promise as a measure of a person’s emotional state. There are many applications where a more quantitative evaluation of an individual’s mental state would be beneficial. For example, in PTSD or depression diagnosis, a quantitative measure to assist and compliment the qualitative assessments conducted by clinicians could reduce the time involved in treatment planning. A better understanding of the underlying mechanism is necessary for building systems that use these signals to assist in critical decision making.
Previous work in emotion research has relied upon averaging in time and then applying standard significance tests and simple classifiers to analyze psychophysiological data. In this work, we extend analysis beyond traditional hypothesis testing and simple classifiers to better understand previous results and design an appropriate computational model. Under the hypothesis that modeling dynamics is important, we design and apply an Input-Output Hidden Markov Model (IOHMM). Through exploration of the learned IOHMM model parameters, we demonstrate the promise that more descriptive, generative machine learning models provide over the more task-specific discriminative models and traditional statistical hypothesis testing. Incorporating time provides an improvement over simple static classifiers in single trial (without averaging in time) prediction accuracy, but does not provide significant improvement over the time averaged results found in literature. To address this, we employ exploratory data analysis methods and examine properties of the algorithms applied to better understand the results and consider improvements. Mutual information computation and clustering provide insight as to the challenges in modeling this data. By applying concepts from learning theory, we show that these seemingly weaker results are actually consistent with previous results. We conclude with insights as to how an alternative approach could elicit more positive results out of this dataset and key theoretical contributions to machine learning that are of value for applying these techniques in scientific research.