Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort

With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users’ states in order to reduce visual strain. We present the first system...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.463-473-037
Main Authors: Hachet, Martin, Lotte, Fabien, Appriou, Aurélien, Frey, Jérémy
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bioengineering
Brain - physiology
Brain Mapping
Brain Waves - physiology
Brain-Computer Interfaces
Channels
Cognitive science
Comfort
Computer Science
Computer Simulation
Depth Perception - physiology
Displays
Electroencephalography
Estimates
Evoked potentials (Electrophysiology)
Female
Graphics
Human-Computer Interaction
Humans
Life Sciences
Machine Learning
Male
Methods
Monitors
Monte Carlo Method
Neuroscience
Photic Stimulation
Questionnaires
Signal and Image Processing
Signal processing
Statistics, Nonparametric
Stereoscopic
Stereoscopy
Surveys and Questionnaires
User-Computer Interface
Visual
Young Adult
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Summary:With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users’ states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials’ (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users’ state by modifying the overlap of left and right images according to the classifier output.
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/2758103