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  1. Home
  3. Machine Learning Performance In Eeg-based Mental Workload Classification Across Task Types: A Systematic Review.
  1. Home
  3. Machine Learning Performance In Eeg-based Mental Workload Classification Across Task Types: A Systematic Review.

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Machine learning performance in EEG-based mental workload classification across task types: a systematic review.

Miloš Pušica1,2, Bogdan Mijović1, Maria Chiara Leva2

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models show lower accuracy in classifying mental workload (MWL) during multitasking compared to single-tasking. This highlights challenges in real-world MWL estimation using electroencephalography (EEG).

Keywords:
deep learningelectroencephalogram (EEG)experimental designmachine learningmental workloadpattern recognitiontask designtask type

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Assessing mental workload (MWL) is crucial for optimizing performance and preventing errors.
  • Existing literature lacks standardized tasks for evaluating MWL estimation models, hindering direct comparison of machine learning (ML) approaches.
  • Electroencephalography (EEG) is a common modality for non-invasively measuring brain activity related to MWL.

Purpose of the Study:

  • To comprehensively examine the performance of ML models in EEG-based MWL classification across different experimental task types.
  • To identify how task complexity, specifically single-tasking versus multitasking, influences the accuracy of MWL estimation.
  • To provide insights into the current state-of-the-art and identify research gaps in EEG-based MWL estimation.

Main Methods:

  • A systematic categorization of existing ML studies based on the task type employed (single-tasking vs. multitasking) and MWL rating method (quantitative vs. subjective).
  • Comparative analysis of the performance metrics (e.g., classification accuracy) of ML models across these categories.
  • Literature review focusing on EEG-based MWL classification studies.

Main Results:

  • A significant decrease in MWL classification accuracy was observed for top-performing ML models in multitasking studies using quantitative task load ratings compared to single-tasking studies.
  • Studies employing subjective MWL ratings showed higher classification accuracy than those using quantitative ratings in multitasking scenarios.
  • Performance disparities underscore the difficulty of accurately estimating MWL in complex, real-world multitasking environments.

Conclusions:

  • Estimating mental workload using EEG-based ML models is significantly more challenging during multitasking, especially when relying on quantitative task load measures.
  • The findings highlight the need for developing more robust ML models capable of handling the complexities of real-world multitasking scenarios.
  • Further research is needed to establish standardized benchmarks and improve MWL estimation accuracy in dynamic and complex task environments.