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Cross-subject mental workload recognition using bi-classifier domain adversarial learning.

Yueying Zhou1,2,3, Pengpai Wang4, Peiliang Gong2,3

  • 1School of Mathematics Science, Liaocheng University, Liaocheng, 252000 China.

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Summary
This summary is machine-generated.

This study introduces a new domain adaptation method for Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems. The approach improves cross-subject accuracy by aligning data both globally and by workload category.

Keywords:
Bi-classifier domain adaptationBrain-computer interfaceCross-subjectElectroencephalogram (EEG)Mental states recognition

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Deploying Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems requires generalizable models applicable across diverse subjects.
  • Existing domain adaptation techniques primarily address global domain discrepancies in EEG data, often overlooking local, workload-categorical divergence.
  • This oversight degrades the performance of subject-invariant features crucial for accurate MWR.

Purpose of the Study:

  • To propose a novel domain adaptation algorithm, category-wise and domain-wise alignment Domain Adaptation (cdaDA), to enhance cross-subject MWR.
  • To address the limitations of existing methods by focusing on both global and local domain discrepancies.
  • To improve the workload-discriminating ability of subject-invariant features in EEG-based MWR.

Main Methods:

  • Developed a joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm.
  • Employed bi-classifier learning to align EEG data within specific mental workload categories, addressing inter-category similarities and differences.
  • Utilized domain discriminative adversarial learning to minimize global domain discrepancy, incorporating global domain information.

Main Results:

  • The cdaDA model integrates both local category-specific and global domain information for a coarse-to-fine alignment of EEG data.
  • Achieved promising results in cross-subject Mental Workload Recognition.
  • Demonstrated improved performance in MWR by effectively mitigating inter-subject discrepancies.

Conclusions:

  • The proposed cdaDA algorithm offers an effective solution for developing generalizable EEG-based MWR systems.
  • Integrating category-wise and domain-wise alignment significantly enhances cross-subject MWR performance.
  • This approach paves the way for more robust and widely applicable MWR technologies.