Journal Article

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Min, Kyungha, Han, Jongdae, Yang, Heekyung

2019

Overview

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Brain research / Classification / Datasets / Deep Learning / Electroencephalography / Emotions / Emotions - physiology / Feature selection / Human subjects / Humans / Memory / Models, Theoretical / Neural networks / Neural Networks, Computer / Principal components analysis / Support vector machines / Time series