Algorithmic Derivation of Human Spatial Navigation Indices From Eye Movement Data

TL;DR

This paper introduces an algorithmic method to derive human spatial navigation indices from eye movement data, enabling better assessment of navigation abilities and early detection of impairments.

Contribution

It presents a novel combination of signal processing and machine learning techniques to extract multiple navigation-related indices from eye movement signals.

Findings

Navigation and orientation index achieved R2 of 0.72

Landmark recognition index achieved R2 of 0.50

Identified eye movement features correlated with navigation skills

Abstract

Spatial navigation is a complex cognitive function involving sensory inputs, such as visual, auditory, and proprioceptive information, to understand and move within space. This ability allows humans to create mental maps, navigate through environments, and process directional cues, crucial for exploring new places and finding one's way in unfamiliar surroundings. This study takes an algorithmic approach to extract indices relevant to human spatial navigation using eye movement data. Leveraging electrooculography signals, we analyzed statistical features and applied feature engineering techniques to study eye movements during navigation tasks. The proposed work combines signal processing and machine learning approaches to develop indices for navigation and orientation, spatial anxiety, landmark recognition, path survey, and path route. The analysis yielded five subscore indices with notable accuracy. Among these, the navigation and orientation subscore achieved an R2 score of 0.72, while the landmark recognition subscore attained an R2 score of 0.50. Additionally, statistical features highly correlated with eye movement metrics, including blinks, saccades, and fixations, were identified. The findings of this study can lead to more cognitive assessments and enable early detection of spatial navigation impairments, particularly among individuals at risk of cognitive decline.