TY  - JOUR
SN  - 16641078
PB  - Frontiers Media S.A.
JF  - Frontiers in Psychology
IS  - DEC
A1  - Babiker, A.
A1  - Faye, I.
A1  - Prehn, K.
A1  - Malik, A.
Y1  - 2015///
TI  - Machine learning to differentiate between positive and negative emotions using pupil diameter
AV  - none
N1  - cited By 19
ID  - scholars6157
N2  - 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.
VL  - 6
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954200761&doi=10.3389%2ffpsyg.2015.01921&partnerID=40&md5=3582e3933fd73b7e0ea4c364bafe0d08
ER  -