2026-06-30T03:04:03Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/184002024-11-28T14:46:50Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927
Repisalud
author
Cabrera, Francisco E.
author
Sánchez-Núñez, Pablo
author
Vaccaro, Gustavo
author
Peláez, José Ignacio
author
Escudero, Javier
authoraffiliation
[Cabrera,FE; Vaccaro,G; Peláez,JI] Department of Languages and Computer Sciences, School of Computer Science and Engineering, Universidad de Málaga, Málaga, Spain. [Cabrera,FE; Sánchez-Núñez,P; Vaccaro,G; Peláez,JI] Centre for Applied Social Research (CISA), Ada Byron Research Building, Universidad de Málaga, Málaga, Spain. [Cabrera,FE; Sánchez-Núñez,P; Vaccaro,G; Peláez,JI] Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain. [Sánchez-Núñez,P] Department of Audiovisual Communication and Advertising, Faculty of Communication Sciences, Universidad de Málaga, Málaga, Spain. [Escudero,J] School of Engineering, Institute for Digital Communications (IDCOM), The University of Edinburgh, Edinburgh, UK.
2024-02-19T15:29:33Z
2024-02-19T15:29:33Z
2021-07-09
10.3390/s21144695
1424-8220
Sensors
http://hdl.handle.net/10668/4562
34300436
http://hdl.handle.net/20.500.12105/18400
The visual design elements and principles (VDEPs) can trigger behavioural changes and emotions in the viewer, but their effects on brain activity are not clearly understood. In this paper, we explore the relationships between brain activity and colour (cold/warm), light (dark/bright), movement (fast/slow), and balance (symmetrical/asymmetrical) VDEPs. We used the public DEAP dataset with the electroencephalogram signals of 32 participants recorded while watching music videos. The characteristic VDEPs for each second of the videos were manually tagged for by a team of two visual communication experts. Results show that variations in the light/value, rhythm/movement, and balance in the music video sequences produce a statistically significant effect over the mean absolute power of the Delta, Theta, Alpha, Beta, and Gamma EEG bands (p < 0.05). Furthermore, we trained a Convolutional Neural Network that successfully predicts the VDEP of a video fragment solely by the EEG signal of the viewer with an accuracy ranging from 0.7447 for Colour VDEP to 0.9685 for Movement VDEP. Our work shows evidence that VDEPs affect brain activity in a variety of distinguishable ways and that a deep learning classifier can infer visual VDEP properties of the videos from EEG activity.
eng
EEG
Emotion classification
CNN
Spectral analysis
Visual perception
Visual attention
Visual features
Visual design elements and principles (VDEPs)
Electroencefalografía
Emociones
Clasificación
Red nerviosa
Cariotipificación espectral
Percepción visual
Pruebas neuropsicológicas
Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks
research article