%0 Journal Article
%@ 16641078
%A Babiker, A.
%A Faye, I.
%A Prehn, K.
%A Malik, A.
%D 2015
%F scholars:6157
%I Frontiers Media S.A.
%J Frontiers in Psychology
%N DEC
%R 10.3389/fpsyg.2015.01921
%T Machine learning to differentiate between positive and negative emotions using pupil diameter
%U https://khub.utp.edu.my/scholars/6157/
%V 6
%X 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. © 2015 Babiker, Faye, Prehn and Malik.
%Z cited By 19