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EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning.

Jun Cao1, Enara Martin Garro1, Yifan Zhao1

  • 1School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) framework for estimating human mental workload. The new method significantly improves classification accuracy using bivariate functional brain connectivity features.

Keywords:
artificial intelligencefeature engineeringmental workloadn-backsensor fusion

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Area of Science:

  • Neuroscience and Cognitive Science
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate estimation of human mental workload is crucial for enhancing productivity and preventing accidents.
  • Existing methods often rely on single physiological sensing modalities and univariate analysis of electroencephalography (EEG) data.
  • There is a need for advanced techniques that integrate multiple data sources and employ sophisticated analytical approaches.

Purpose of the Study:

  • To propose a novel framework for multi-level mental workload classification using hybrid EEG-functional near-infrared spectroscopy (fNIRS) data.
  • To investigate the efficacy of bivariate functional brain connectivity (FBC) features in the time and frequency domains for EEG analysis.
  • To leverage machine learning for improved mental workload estimation.

Main Methods:

  • Utilized a hybrid approach combining EEG and fNIRS physiological sensing modalities.
  • Employed bivariate functional brain connectivity (FBC) features in delta, theta, and alpha frequency bands for EEG analysis, moving beyond univariate power spectral density (PSD).
  • Integrated fNIRS oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) indicators and applied machine learning algorithms for classification.

Main Results:

  • The hybrid EEG-fNIRS framework achieved significant improvements in mental workload classification accuracy: 77% for 0-back vs. 2-back and 83% for 0-back vs. 3-back on a public dataset.
  • Topographic and heat-map visualizations revealed distinct brain regions contributing to workload discrimination between EEG and fNIRS.
  • Identified optimal regions for discrimination: posterior midline occipital (POz) for alpha-band EEG and the right frontal region (AF8) for fNIRS.

Conclusions:

  • The proposed hybrid EEG-fNIRS framework with bivariate FBC features offers a robust approach for estimating multi-level mental workload.
  • The study highlights the complementary nature of EEG and fNIRS, with different brain regions showing superiority for each modality in workload assessment.
  • This research provides a foundation for developing more accurate and reliable mental workload monitoring systems.