Gaze estimation using EEG signals for HCI in augmented and virtual reality headsets

Augmented and virtual reality have evolved significantly over the last few years providing new ways of entertainment and interaction with the environment. Although many systems and solutions are currently available, still there is much left unsettled and some technologies are missing from many VR/AR devices, such as foveated rendering and HCI. In this paper a novel approach for coarse gaze estimation using EEG sensors with applications in items selection for HCI or foveated rendering for VR/AR devices is proposed. The suggested method requires only few electroencephalogram sensors that can be easily added to the current virtual and augmented reality headsets. A supervised machine leaning approach was suggested utilising novel features, based on quaternions allowing gaze estimation. Experiments were performed to evaluate the proposed method and a new dataset was designed and captured. Finally, the introduced learning framework was compared with other similar techniques demonstrating further the gain of the proposed descriptors.