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.

The influence of dissolved oxygen (DO) and Shewanella algae on the corrosion behavior of pure titanium were systematically studied in this work. The formation of S. algae biofilms on titanium surface was facilitated by the anaerobic environment, which accelerated titanium corrosion. Upon the breakdown of passive film, S. algae acquired electrons from the titanium base substrate through extracellular electron transfer (EET) processes. Various EET-related genes were overexpressed by the low DO condition, leading to enhanced EET kinetics. High concentration DO increased TiO2 content and the thickness of passive film, which enhanced the protective effect and mitigated microbiologically influenced corrosion.