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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

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Published on: December 5, 2025

Mental workload classification using heart rate metrics.

Andreas Henelius¹, Kati Hirvonen, Anu Holm

¹Brain Work Research Centre, Finnish Institute of Occupational Health, Helsinki, Finland. andreas.henelius@ttl.fi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

Summary

This summary is machine-generated.

Heart rate variability metrics can classify mental workload. The average interbeat interval length best identified workload levels during cognitive tasks.

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Area of Science:

Neuroscience
Psychophysiology
Human-Computer Interaction

Background:

Mental workload (MWL) assessment is crucial for performance optimization and safety.
Heart rate variability (HRV) and event-related potentials (ERPs) are physiological measures sensitive to cognitive load.
Previous research has explored various physiological markers for MWL, but short-term classification remains challenging.

Purpose of the Study:

To evaluate the efficacy of short-term heart rate variability (HRV) metrics in classifying mental workload (MWL) levels.
To compare the classification performance of different HRV metrics using objective electroencephalographic (EEG) data.
To identify the most effective HRV metric for real-time MWL assessment.

Main Methods:

Collected electrocardiographic (ECG) and electroencephalographic (EEG) data from 13 healthy participants performing a computerized cognitive multitask test.
Utilized the P300 component amplitude from event-related potentials (ERPs) as an objective measure of MWL.
Analyzed 140-second segments of data to assess short-term HRV metrics and their classification accuracy using Receiver Operating Characteristics (ROC) analysis.

Main Results:

The average interbeat interval length, a time-domain HRV metric, demonstrated the highest classification ability for MWL.
Other short-term HRV metrics showed varying degrees of success in differentiating workload levels.
P300 amplitude served as a reliable objective indicator of MWL during the cognitive tasks.

Conclusions:

Short-term HRV, particularly the average interbeat interval length, is a viable and effective non-invasive method for classifying mental workload.
HRV metrics offer a promising avenue for developing real-time monitoring systems for cognitive load.
Integrating HRV with EEG-derived measures like P300 provides a robust approach to understanding and quantifying MWL.