Babiker Areej, Faye Ibrahima, Prehn Kristin, Malik Aamir
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.
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.
Front Psychol. 2015 Dec 22;6:1921. doi: 10.3389/fpsyg.2015.01921. eCollection 2015.
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.
瞳孔直径（PD）已被认为是识别个体情绪状态的可靠参数。在本文中，我们引入一种学习机器技术来检测和区分积极情绪和消极情绪。我们向30名参与者呈现积极和消极的声音刺激，并记录瞳孔反应。结果显示，在处理消极和积极声音刺激时，瞳孔扩张显著增加，消极刺激的增加幅度更大。我们还发现，在试验结束时，消极刺激比积极刺激的瞳孔扩张更持久，利用机器学习方法来区分积极和消极情绪，其准确率为96.5%，敏感度为97.93%，特异性为98%。使用为另一项不同研究设计的另一个数据集对所得结果进行了验证，该数据集是在30名参与者处理带有积极和消极情绪的单词对时记录的。
