Evaluation of EEG-Based Predictions of Image QoE in Augmented Reality Scenarios
Brian Bauman, Patrick Seeling · 2018
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.