Towards stereoscopic vision: Attention-guided gaze estimation with EEG in 3D space

Since traditional gaze-tracking methods rely on line-of-sight estimation, spatial attention modeling from neural activity offers an alternative perspective to gaze estimation. This paper presents a proof-of-concept study on attention-guided gaze estimation with Electroencephalography (EEG), investigating whether brain signals can be leveraged to estimate attentional focus within a controlled 3D environment. We first conducted a preliminary survey to gather public opinions, revealing a generally positive attitude towards EEG-driven gaze tracking. Building on this insight, we collected an EEG dataset in VR, where participants engaged with stimuli presented at predefined spatial locations. We introduce a deep learning model that estimates the relative saliency of candidate positions, enabling gaze estimation through optimization within the learned representation. Our results demonstrate that attentional focus was successfully mapped in a 3D coordinate space from 5 participants, and low-frequency oscillations contributed more significantly to predictive performance. The model achieved robust accuracy in distinguishing gaze locations, highlighting the potential of EEG-based gaze estimation for attention tracking in 3D environments. • We present a proof-of-concept study on attention-guided gaze estimation with EEG, validating the feasibility of mapping EEG activity to gaze positions beyond 2D settings. • We introduce a neural decoding framework that leverages a Transformer-based encoder and iterative refinement to estimate the most salient point of regard (PoR) in predefined spatial locations. • We contribute a new EEG dataset collected in a VR environment with stereoscopic stimuli, which can support future research on neural-based gaze estimation and attention modeling in immersive settings.