eprintid: 6157
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/61/57
datestamp: 2023-11-09 16:17:54
lastmod: 2023-11-09 16:17:54
status_changed: 2023-11-09 16:05:04
type: article
metadata_visibility: show
creators_name: Babiker, A.
creators_name: Faye, I.
creators_name: Prehn, K.
creators_name: Malik, A.
title: Machine learning to differentiate between positive and negative emotions using pupil diameter
ispublished: pub
note: cited By 19
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. © 2015 Babiker, Faye, Prehn and Malik.
date: 2015
publisher: Frontiers Media S.A.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954200761&doi=10.3389%2ffpsyg.2015.01921&partnerID=40&md5=3582e3933fd73b7e0ea4c364bafe0d08
id_number: 10.3389/fpsyg.2015.01921
full_text_status: none
publication: Frontiers in Psychology
volume: 6
number: DEC
refereed: TRUE
issn: 16641078
citation:   Babiker, A. and Faye, I. and Prehn, K. and Malik, A.  (2015) Machine learning to differentiate between positive and negative emotions using pupil diameter.  Frontiers in Psychology, 6 (DEC).   ISSN 16641078