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Related Experiment Video

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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

Estimating brain load from the EEG.

Anu Holm¹, Kristian Lukander, Jussi Korpela

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

Thescientificworldjournal

|July 21, 2009

Summary

This summary is machine-generated.

A new "brainbeat" index using electroencephalography (EEG) can measure cognitive overload and mental fatigue during work. This brain monitoring tool shows increased load with multitasking and prolonged wakefulness.

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Area of Science:

Neuroscience
Cognitive Science
Human Factors Engineering

Background:

Modern work demands high cognitive function, leading to stress and errors.
Objective, real-time methods for assessing cognitive overload and mental fatigue are lacking.
Existing methods are often invasive or not suitable for continuous monitoring during work.

Purpose of the Study:

To develop and validate a novel, non-invasive index for on-line estimation of cognitive load and mental fatigue.
To assess the index's responsiveness to acute task load and cumulative effects of wakefulness.
To explore the potential for wearable implementation of the proposed index.

Main Methods:

Utilized a two-channel electroencephalography (EEG) setup.
Calculated the theta Fz/alpha Pz ratio as a measure of cognitive state.
Examined the index's correlation with concurrent task load and time awake (normal and sleep-restricted conditions).

Main Results:

The theta Fz/alpha Pz ratio significantly increased with the number of concurrent tasks (p = 0.004).
The index also increased with prolonged wakefulness, both after normal sleep (p = 0.002) and sleep restriction (p = 0.004).
A more pronounced increase was observed in the afternoon following sleep restriction (p = 0.006).

Conclusions:

The theta Fz/alpha Pz ratio, termed "brainbeat", effectively reflects cognitive overload and mental fatigue.
This index shows potential for real-time monitoring of brain states during demanding work.
The "brainbeat" index could be implemented in wearable devices for objective assessment of cognitive load.