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Eye-Tracking and Skin Conductance to Monitor Cognitive Task Engagement.

Agustina Fragueiro^1,2, Julie Fournier^1,3, René-Paul Debroize¹

¹Université de Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, EMPENN-ERL U 1228, Rennes, France.

Psychophysiology

|April 30, 2026

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This study monitored cognitive engagement using eye tracking and electrodermal activity to improve neurofeedback efficacy. Physiological signals accurately predicted task performance, offering potential for personalized brain rehabilitation.

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Neurofeedback shows promise for brain rehabilitation and cognitive enhancement.
A significant inefficacy problem exists, with over a third of participants failing to learn self-regulation.
Lack of task engagement, potentially due to inappropriate difficulty, is a suspected cause of neurofeedback failure.

Purpose of the Study:

To explore monitoring cognitive engagement using physiological signals (eye tracking, electrodermal activity).
To develop a model predicting task performance based on physiological indicators of engagement.
To enhance the efficacy of neurofeedback by addressing engagement issues.

Main Methods:

Recorded eye-tracking and electrodermal activity signals during cognitive tasks.

Keywords:

GSR cognitive engagement eye‐tracking neurofeedback pupil diameter skin conductance response sustained attention

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Designed tasks to manipulate cognitive load and attentional focus.

Trained a linear model to predict participant performance using physiological signals.

Main Results:

Pupil diameter effectively distinguished focused attention from mind-wandering.
Pupil diameter and skin conductance response differentiated task vs. rest conditions.
The model accurately predicted performance across different tasks, demonstrating generalizability.

Conclusions:

Physiological signals like pupil diameter and skin conductance are reliable indicators of cognitive engagement.
A predictive model based on these signals can monitor engagement and forecast performance.
This approach holds potential for developing individualized neurofeedback protocols to improve rehabilitation outcomes.