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

Self Cite

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, which show the internal structure of the human body (including blood, flesh, muscle, fatty tissue, and bone) act as an obstacle in explaining the images to patients or communicating with the images with non-professional people. We claim that the negative emotional reactions generated by illustrated surgical images are less severe than those caused by raw surgical images. To demonstrate the difference in emotional reaction, we produce several illustrated surgical images from photographs and measure the emotional reactions they engender using EEG biosignals; a deep learning-based emotion recognition model is applied to extract emotional reactions. Through this experiment, we show that the negative emotional reactions associated with photographic surgical images are much higher than those caused by illustrated versions of identical images. We further execute a self-assessed user survey to prove that the emotions recognized from EEG signals effectively represent user-annotated emotions.

Section: Discussionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Deep Emotion Recognition Modelmentioning

confidence: 99%

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EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images

Yang

Han

Min

2020

Self Cite

“…They compared their performance with the existing studies and showed better accuracy for valence and arousal. They also extended their emotion recognizer to apply the task of distinguishing emotional responses to photographs and artwork [19].…”

Section: Deep Learning-based Modelsmentioning

confidence: 99%

Emotion Variation from Controlling Contrast of Visual Contents through EEG-Based Deep Emotion Recognition

Yang

Han

Min

2020

Self Cite

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography (EEG) signals. As a result, we conclude that the enhancement of contrast induces the increase of valence, while the reduction of contrast induces the decrease. Meanwhile, the contrast control affects arousal on a very minute scale.

“…Deep learning is a particularly popular research method nowadays. It can learn the internal laws and representation levels of sample data through multi-layer neural networks, and it has been widely used in many fields in recent years [ 38 , 39 , 40 , 41 , 42 , 43 ]. At the same time, neural networks are also considered as a useful method for unsteady aerodynamic modeling.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Chen

et al. 2020

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.