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Journal Articles Computational Intelligence and Neuroscience Year : 2016

Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort

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Abstract

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.
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Dates and versions

hal-01222045 , version 1 (29-10-2015)

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Jérémy Frey, Aurélien Appriou, Fabien Lotte, Martin Hachet. Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort. Computational Intelligence and Neuroscience, 2016, 2016, ⟨10.1155/2016/2758103⟩. ⟨hal-01222045⟩
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