Pupillometry and Brain Dynamics for Cognitive Load in Working Memory

TL;DR

This study addresses the need for practical, real-time cognitive-load monitoring by comparing pupillometry and EEG using a lightweight, interpretable feature-based pipeline. Leveraging Catch-22 time-series features and SHAP for interpretability, the authors show that pupillometry can match EEG performance on cognitive-load classification, offering a wearable-friendly proxy. XGBoost and Random Forest consistently deliver strong accuracy with low computational cost, while deep learning approaches lag in efficiency and interpretability. The findings support scalable, real-time cognitive monitoring for neuropsychiatry, education, and healthcare and suggest multimodal fusion and domain adaptation to enhance robustness in real-world settings.

Abstract

Cognitive load, the mental effort required during working memory, is central to neuroscience, psychology, and human-computer interaction. Accurate assessment is vital for adaptive learning, clinical monitoring, and brain-computer interfaces. Physiological signals such as pupillometry and electroencephalography are established biomarkers of cognitive load, but their comparative utility and practical integration as lightweight, wearable monitoring solutions remain underexplored. EEG provides high temporal resolution of neural activity. Although non-invasive, it is technologically demanding and limited in wearability and cost due to its resource-intensive nature, whereas pupillometry is non-invasive, portable, and scalable. Existing studies often rely on deep learning models with limited interpretability and substantial computational expense. This study integrates feature-based and model-driven approaches to advance time-series analysis. Using the OpenNeuro 'Digit Span Task' dataset, this study investigates cognitive load classification from EEG and pupillometry. Feature-based approaches using Catch-22 features and classical machine learning models outperform deep learning in both binary and multiclass tasks. The findings demonstrate that pupillometry alone can compete with EEG, serving as a portable and practical proxy for real-world applications. These results challenge the assumption that EEG is necessary for load detection, showing that pupil dynamics combined with interpretable models and SHAP based feature analysis provide physiologically meaningful insights. This work supports the development of wearable, affordable cognitive monitoring systems for neuropsychiatry, education, and healthcare.

Figures (3)

• Figure 1: Biphasic and Triphasic Models of Pupil Response to Increasing Cognitive Load b1
• Figure 2: Flowchart outlining Methodological Pipeline.
• Figure 3: Experimental design of the Digit Span working memory task