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

Abstract

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.

The neurophysiological basis of leadership: a machine learning approach

“…For visual stimulation, we employed an ad hoc task comprising of participants watching video clips. Although still images from the International Affective Picture System (IAPs) used to be utilized to evoke emotional response in study participants, researchers recently began using video content instead, attributing to the oldness of IAPs images and because even their recreations, in many cases, do not arouse the required emotions (Abrisqueta-Gomez et al ., 2002; Yang et al ., 2020). A video-based emotion-triggering approach offers rich visual and auditory stimulation content to trigger specific emotions that can be captured using EEG and GSR (Hu et al ., 2020).…”

Intelligent Biosignal Analysis Methods

“…The paper [ 3 ], titled “EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images”, was authored by H. Yang, J. Han, and K. Min, from Sangmyung University, Korea.…”

Entornos de realidad virtual y neurociencia organizacional para la evaluación de capacidades de liderazgo en organizaciones

“…Por ello se determinó el uso de contenido visual dinámico (videos) elaborados por nosotros mismos con contenido emocional. Para la validación de los mismos, y como en el caso anterior con las IAPS, se llevó a cabo una validación interna (Abrisqueta -Gomez, Yang, H. et al, 2020) mediante un estudio piloto, en el cual se determinó el contenido Estudio 1. Sensibilidad de medidas fisiológicas y neurofisiológicas frente a estimulos emocionales no interactivos para discriminar entre estilos de liderazgo emocional pidiendo a voluntarios (estudiantes universitarios) que calificaran cada video con carga emocional o neutra y después etiquetaran estas emociones.…”