Seeing the Forrest through the trees: Oculomotor metrics are linked to heart rate

Pilot study on the dynamic interactions between cardiac activity and corneal biomechanics during eye movements

Background and objective

The study examines the relationship between ocular rotations and cardiovascular functions through detailed biomechanical analysis. The study documents specific patterns of ocular movements and their synchronization with cardiovascular activity, highlighting significant correlations. These findings provide a basis for understanding the opto-biomechanical interplay between ocular and cardiovascular dynamics.

Methods

Authors employed a custom-designed prototype, integrating a camera and numerical pulse oximeter, to analyze the right eyeballs of participants. The corneal surface reflections were recorded, along with concurrent blood pulsation (BP) signal acquisition. Numerical analysis helped determine the reflection positions and orientations to study eye movements and corneal deformations. Time and frequency domain analyses, including coherence functions, were utilized.

Results

Significant correlations were found between variations in corneal curvature, selected fixational eye movements (FEM) parameters, and Pulsatile ocular blood flow (POBF), revealing synchronized expansions of the corneal surfaces with cardiovascular activity. Furthermore, FEM displayed spectral correlations with BP, indicating an interrelation between ocular rotations and cardiovascular functions.

Conclusion

These findings reveal the complex interactions between the cornea and Pulsatile Ocular Blood Flow (POBF), as well as between Fixational Eye Movements (FEM) and POBF. While the influence of POBF on both corneal dynamics and FEM is clear, further research is necessary to directly link corneal dynamics and FEM. These insights hold potential for non-invasive diagnostic applications and provide a deeper understanding of ocular biomechanics.

Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach

Objectives

Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reflecting specific adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks.

Methods

Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions.

Results

Cluster analysis of pupillometry data identified two distinct groups with statistically significant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower self-esteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1. Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were significant between the clusters.

Conclusion

The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulation, cognitive load, and behavioral responses to cognitive load and deceptive feedback. These findings underscore the potential of pupillometry combined with machine learning in identifying individual differences in stress resilience and cognitive performance. Our research on pupillary dynamics and ANS patterns can lead to the development of remote diagnostic tools for real-time cognitive stress monitoring and performance optimization, applicable in clinical, educational, and occupational settings.

What is a blink? Classifying and characterizing blinks in eye openness signals

Blinks, the closing and opening of the eyelids, are used in a wide array of fields where human function and behavior are studied. In data from video-based eye trackers, blink rate and duration are often estimated from the pupil-size signal. However, blinks and their parameters can be estimated only indirectly from this signal, since it does not explicitly contain information about the eyelid position. We ask whether blinks detected from an eye openness signal that estimates the distance between the eyelids (EO blinks) are comparable to blinks detected with a traditional algorithm using the pupil-size signal (PS blinks) and how robust blink detection is when data quality is low. In terms of rate, there was an almost-perfect overlap between EO and PS blink (F1 score: 0.98) when the head was in the center of the eye tracker's tracking range where data quality was high and a high overlap (F1 score 0.94) when the head was at the edge of the tracking range where data quality was worse. When there was a difference in blink rate between EO and PS blinks, it was mainly due to data loss in the pupil-size signal. Blink durations were about 60 ms longer in EO blinks compared to PS blinks. Moreover, the dynamics of EO blinks was similar to results from previous literature. We conclude that the eye openness signal together with our proposed blink detection algorithm provides an advantageous method to detect and describe blinks in greater detail.