RT Journal Article
T1 Impact of visual design elements and principles in human electroencephalogram brain activity assessed with spectral methods and convolutional neural networks
A1 Cabrera, Francisco E.
A1 Sánchez-Núñez, Pablo
A1 Vaccaro, Gustavo
A1 Peláez, José Ignacio
A1 Escudero, Javier
AB 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.
PB MDPI
SN 1424-3210
YR 2021
FD 2021-07-09
LK https://hdl.handle.net/10016/39114
UL https://hdl.handle.net/10016/39114
LA eng
NO This research was partially supported by On the Move, an international mobility programme organized by the Society of Spanish Researchers in the United Kingdom (SRUK) and CRUE Universidades Españolas. The Article Processing Charge (APC) was funded by the Programa Operativo Fondo Europeo de Desarrollo Regional (FEDER) Andalucía 2014–2020 under Grant UMA 18-FEDERJA-148 and Plan Nacional de I+D+i del Ministerio de Ciencia e Innovación-Gobierno de España (2021-2024) under Grant PID2020-115673RB-100.
DS e-Archivo
RD 1 jul. 2024