title: Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter.
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.
authors.affiliation: Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia; Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia.
authors.forename: Areej
authors.initials: A
authors.lastname: Babiker
authors.affiliation: Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia; Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia.
authors.forename: Ibrahima
authors.initials: I
authors.lastname: Faye
authors.affiliation: Department of Neurology and NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin Berlin, Germany.
authors.forename: Kristin
authors.initials: K
authors.lastname: Prehn
authors.affiliation: Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia; Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONASBandar Seri Iskandar, Malaysia.
authors.forename: Aamir
authors.initials: A
authors.lastname: Malik
keywords: classification; emotion recognition; k-nearest neighbor algorithm; pupillometry; sensitivity analysis