@inproceedings{c85edeedfcff4614ae0c8fb6520747ae,
title = "Evaluation of EEG-Based Predictions of Image QoE in Augmented Reality Scenarios",
abstract = "Augmented Reality (AR) devices and phone adaptations are commonly head-worn to overlay context-dependent information into the field of view of the device operators. One particular scenario is the overlay of still images, for which we evaluate the interplay of user ratings as Quality of Experience (QoE) with (i) the non-referential BRISQUE objective image quality metric as QoS and (ii) human subject dry electrode EEG signals gathered with a commercial device. We find strong correlations for subject-specific EEG portfolios, resulting in an approach to the predictability of the QoE. Our overall results can be employed in practical scenarios by mobile content and network service providers to optimize the user experience in augmented reality scenarios with a passive human in-the-loop in the future.",
keywords = "Augmented reality, Electroencephalography, Image quality, Quality of experience, Quality of service",
author = "Brian Bauman and Patrick Seeling",
note = "Funding Information: This material is based upon work supported by the Faculty Research and Creative Endeavors (FRCE) program at Central Michigan University under grant #48146. Publisher Copyright: {\textcopyright} 2018 IEEE.; null ; Conference date: 27-08-2018 Through 30-08-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/VTCFall.2018.8690566",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings",
}