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Related Experiment Video

Updated: Aug 23, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning.

Luca Longo^1,2,3

¹Artificial Intelligence and Cognitive Load Research Lab, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland.

|October 27, 2022

Summary

This summary is machine-generated.

A new deep learning method models mental workload from EEG data, offering a generalizable approach without human input. This self-supervised technique identifies cognitive activation patterns, improving performance prediction.

Keywords:

EEG bands brain rate cognitive load convolutional neural network deep learning electroencephalography mental workload recurrent neural network self-supervision spectral topology-preserving head-maps

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Last Updated: Aug 23, 2025

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Cognitive Science
Neuroscience
Machine Learning

Background:

Accurate mental workload assessment is crucial for predicting human performance.
Existing methods lack general applicability due to differing definitions and models.
A universally accepted method for quantifying cognitive load is still needed.

Purpose of the Study:

To present a novel, self-supervised deep learning method for mental workload modeling.
To automatically induce models from electroencephalography (EEG) data, enhancing replicability and generalizability.
To develop a computational approach independent of human declarative knowledge.

Main Methods:

Utilized a convolutional recurrent neural network (CRNN) trained on spectral topographic head-maps from EEG data.
Employed a continuous brain rate, an index of cognitive activation, as the target variable.
Developed self-supervised models without requiring human-crafted features or labels.

Main Results:

Within-subject models achieved an average Mean Absolute Percentage Error of approximately 11% in predicting cognitive activation.
Convolutional layers effectively learned high-level representations from EEG data.
Across-subject models demonstrated comparable accuracy to within-subject models, suggesting generalizable cognitive activation patterns.

Conclusions:

The proposed deep learning method offers a generally applicable computational tool for mental workload modeling.
Findings indicate the existence of quasi-stable, subject-independent cognitive activation patterns.
This approach advances the field by providing an automated, data-driven alternative to traditional, ad hoc models.