2026-06-30T03:03:59Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/184002024-11-28T14:46:50Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927

00925njm 22002777a 4500 dc Cabrera, Francisco E. author Sánchez-Núñez, Pablo author Vaccaro, Gustavo author Peláez, José Ignacio author Escudero, Javier author 2021-07-09 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. 10.3390/s21144695 1424-8220 Sensors http://hdl.handle.net/10668/4562 34300436 http://hdl.handle.net/20.500.12105/18400 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