AUTHOR=Babiker Areej , Faye Ibrahima , Prehn Kristin , Malik Aamir TITLE=Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter JOURNAL=Frontiers in Psychology VOLUME=6 YEAR=2015 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2015.01921 DOI=10.3389/fpsyg.2015.01921 ISSN=1664-1078 ABSTRACT=

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual’s emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.