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Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for

Hongzuo Chu1,2, Yong Cao1, Jin Jiang1

  • 1National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China.

Biomedical Engineering Online
|February 3, 2022
PubMed
Summary

Optimized electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) configurations improve mental workload detection accuracy. This enhanced multimodal approach offers a more practical solution for complex human-computer interaction systems.

Keywords:
EEGMan–machine systemsMental workloadfNIRS

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Mental workload is crucial in designing complex man-machine systems.
  • Existing electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) methods for mental workload detection have limitations, including complex setups and lower accuracy.
  • Multimodal detection integrating EEG and fNIRS shows promise but requires optimization.

Purpose of the Study:

  • To optimize the signal acquisition configuration for EEG-fNIRS-based mental workload detection.
  • To develop a more accurate and convenient method for detecting mental workload.
  • To enhance the practical applicability of mental workload detection technologies.

Main Methods:

  • Optimized signal acquisition configuration based on feature importance analysis in a mental workload recognition model.
  • Conducted a Multi-Task Attribute Battery (MATB) task with 20 volunteers.
  • Collected subjective scale data, 64-channel EEG data, and two-channel fNIRS data.

Main Results:

  • Higher EEG channel counts correlate with increased detection accuracy, with diminishing returns beyond 26 channels.
  • Achieved a four-level mental workload detection accuracy of 76.25% ± 5.21%.
  • Physiological analysis confirmed increased θ power (EEG) and O2Hb concentration (fNIRS) with task difficulty, alongside decreased HHb. Novel findings include significant changes in occipital EEG bands and O2Hb amplitude with task difficulty.

Conclusions:

  • Optimized EEG-fNIRS configuration uses 26 EEG channels and two frontal fNIRS channels.
  • The proposed method achieves a higher accuracy (76.25% ± 5.21%) than previous studies.
  • This optimized configuration is suitable for promoting mental workload detection in military, driving, and other complex human-computer interaction systems.