Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral Methods and Convolutional Neural Networks

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (14), p.4695
Main Authors: Cabrera, Francisco E., Sánchez-Núñez, Pablo, Vaccaro, Gustavo, Peláez, José Ignacio, Escudero, Javier
Format: Article
Language:English
Subjects:
Aesthetics
Asymmetry
Brain research
CNN
Datasets
Design
Designers
EEG
Electroencephalography
emotion classification
Emotions
Neural networks
Neurosciences
Principles
Semantics
spectral analysis
Spectral methods
Symmetry
Video data
visual attention
Visual communication
Visual perception
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21144695