Affect prediction from physiological measures via visual stimuli

This study aims to predict different affective states via physiological measures with three types of computational models. An experiment was designed to elicit affective states with standardized affective pictures when multiple physiological signals were measured. Three data mining methods (i.e., decision rules, k-nearest neighbours, and decomposition tree) based on the rough set technique were then applied to construct prediction models from the extracted physiological features. We created three types of prediction models, i.e., gender-specific (male vs. female), culture-specific (Chinese vs. Indian vs. Western), and general models (participants with different genders and cultures as samples), and direct comparisons were made among these models. The best average prediction accuracies in terms of the F1 measures (the harmonic mean of precision and recall) were 60.2%, 64.9%, 63.5% for the general models with 14, 21, and 42 samples, 78.0% for the female models, 75.1% for the male models, 72.0% for the Chinese models, 73.0% for the Indian models, and 76.5% for the Western models, respectively. These results suggested that the specific models performed better than did the general models.

► Pictures from the IAPS can effectively elicit affective states.
► Physiological measures are able to discriminate affective states with advanced computational models.
► Physiological measures are able to discriminate affective states with advanced computational models.
► Physiological specificity in the same gender group or the same cultural group is higher than that in the general group.