A machine learning approach to predict emotional arousal and valence from gaze extracted features

Vasileios Skaramagkas, Emmanouil Ktistakis, D. Manousos, N. Tachos, E. Kazantzaki, E. Tripoliti, D. Fotiadis, M. Tsiknakis
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引用次数: 2

Abstract

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.
从凝视提取的特征中预测情绪唤醒和价态的机器学习方法
在过去的几年里,许多研究一直在调查情绪唤起和效价。他们中的大多数都集中在使用生理信号,如脑电图或肌电图,心血管测量或皮肤电导。然而,与眼睛相关的特征已被证明是非常有用且易于使用的指标,尤其是瞳孔大小和眨眼活动。本研究的目的是预测在情绪激动的情况下由眼睛相关特征引起的情绪唤醒和效价水平。出于这个原因,我们进行了一项实验研究,参与者观看了引发情绪的视频,并自我评估了他们的情绪,同时记录了他们的眼球运动。在这项工作中,几种分类器,如KNN, SVM,朴素贝叶斯,树和集成方法进行了训练和测试。最后,情绪唤醒和效价水平的预测效率分别为85%和91%。
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