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

publication date

  • July 2021

start page

  • 4695

issue

  • 14

volume

  • 21

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

abstract

  • 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.

subjects

  • Information Science

keywords

  • eeg; emotion classification; cnn; spectral analysis; visual perception; visual attention; visual features; visual design elements and principles (vdeps)