@article{scholars6157,
            year = {2015},
       publisher = {Frontiers Media S.A.},
         journal = {Frontiers in Psychology},
             doi = {10.3389/fpsyg.2015.01921},
          volume = {6},
            note = {cited By 19},
          number = {DEC},
           title = {Machine learning to differentiate between positive and negative emotions using pupil diameter},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954200761&doi=10.3389\%2ffpsyg.2015.01921&partnerID=40&md5=3582e3933fd73b7e0ea4c364bafe0d08},
        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. {\^A}{\copyright} 2015 Babiker, Faye, Prehn and Malik.},
          author = {Babiker, A. and Faye, I. and Prehn, K. and Malik, A.},
            issn = {16641078}
}