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Impact of Visual Design Elements and Principles in Human Electroencephalogram Brain Activity Assessed with Spectral
Francisco E Cabrera1,2,3, Pablo Sánchez-Núñez2,3,4, Gustavo Vaccaro1,2,3
1Department of Languages and Computer Sciences, School of Computer Science and Engineering, Universidad de Málaga, 29071 Málaga, Spain.
Sensors (Basel, Switzerland)
|July 24, 2021
Summary
Visual design elements like color, light, movement, and balance significantly impact brain activity, as measured by electroencephalogram (EEG) signals. A deep learning model can predict these visual elements from viewer EEG data.
Area of Science:
- Neuroscience
- Visual Communication
- Human-Computer Interaction
Background:
- Visual design elements and principles (VDEPs) influence emotions and behavior.
- The precise impact of VDEPs on brain activity remains unclear.
- Understanding this relationship can enhance user experience and design strategies.
Purpose of the Study:
- To investigate the relationship between specific VDEPs (color, light, movement, balance) and brain activity.
- To determine if electroencephalogram (EEG) signals can be used to infer VDEP properties.
- To explore the potential of deep learning in analyzing VDEP-induced neural responses.
Main Methods:
- Utilized the public DEAP dataset, recording EEG signals from 32 participants watching music videos.
- Manually tagged VDEPs (color, light, movement, balance) for each second of video content.
- Trained a Convolutional Neural Network (CNN) to predict VDEPs from EEG data.
Main Results:
- Variations in light, movement, and balance significantly affected EEG power across multiple frequency bands (Delta, Theta, Alpha, Beta, Gamma).
- The CNN achieved high accuracy in predicting VDEPs from EEG signals, with Movement VDEP prediction reaching 0.9685.
- Color VDEP prediction accuracy was 0.7447, demonstrating the model's capability across different VDEPs.
Conclusions:
- VDEPs demonstrably affect brain activity in distinct ways.
- EEG signals contain information that allows for the inference of visual properties of stimuli.
- Deep learning models can effectively decode VDEP characteristics from neural data, opening avenues for objective visual design analysis.
