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Tracking of Mental Workload with a Mobile EEG Sensor.

Ekaterina Kutafina1,2, Anne Heiligers3, Radomir Popovic1

  • 1Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary

This study shows that a mobile electroencephalography (EEG) setup effectively tracks mental workload during cognitive tasks. Findings suggest this technology can evaluate cognitive training by detecting workload changes with 86% accuracy.

Keywords:
EEGN-backcognitive effortmHealthwearable

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Mental workload is crucial for learning and motivation.
  • Evaluating cognitive training requires reliable workload assessment.
  • Mobile electroencephalography (EEG) offers a potential solution for real-time workload monitoring.

Purpose of the Study:

  • To assess the feasibility of a mobile EEG setup for tracking mental workload.
  • To determine if mobile EEG can detect changes in cognitive load during a task.
  • To evaluate the potential of mobile EEG in assessing cognitive training.

Main Methods:

  • Twenty-five healthy subjects performed a three-level N-back test using a fully mobile EEG setup.
  • Data analysis included analysis of variance and artificial neural networks.
  • EEG data was collected using a self-mounted mobile EEG device.

Main Results:

  • The mobile EEG setup successfully detected changes in cognitive load.
  • Specific changes observed include decreased occipital alpha and increased frontal, parietal, and occipital theta activity with higher cognitive load.
  • Machine learning models discriminated distinct cognitive load levels with 86% accuracy.

Conclusions:

  • A mobile EEG setup is feasible for monitoring mental workload.
  • This technology can identify cognitive load variations across different brain regions and frequency bands.
  • Mobile EEG shows promise for evaluating cognitive training approaches.