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Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional

Anmol Gupta1, Ronnie Daniel2, Akash Rao3

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India.

Big Data
|February 27, 2023
PubMed
Summary
This summary is machine-generated.

This study shows that combining functional connectivity algorithms with deep learning, specifically Phase Transfer Entropy (PTE) and BrainNetCNN, can accurately classify cognitive workload levels from electroencephalogram (EEG) data, achieving 99.50% accuracy.

Keywords:
BrainNetCNNcognitive workloadfunctional connectivity analysismutual informationpassive BCIphase transfer entropy

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer potential for controlling devices and proactive health management.
  • EEG signals are inherently noisy and variable, posing challenges for real-time data processing and robust cognitive state monitoring.
  • Existing methods struggle to accurately reflect neuronal dynamics amidst high EEG variability, particularly for passive BCIs that track cognitive workload.

Purpose of the Study:

  • To evaluate the efficacy of combining functional connectivity algorithms with deep learning for classifying cognitive workload levels.
  • To compare the performance of Phase Transfer Entropy (PTE) and Mutual Information (MI) as functional connectivity measures.
  • To assess the classification accuracy of the BrainNetCNN deep learning model using these connectivity measures.

Main Methods:

  • Acquired 64-channel EEG data from 23 participants performing the n-back task at low (1-back), medium (2-back), and high (3-back) workload conditions.
  • Extracted functional connectivity matrices using Phase Transfer Entropy (PTE) and Mutual Information (MI).
  • Classified cognitive workload levels using the BrainNetCNN deep learning model.

Main Results:

  • Mutual Information (MI) with BrainNetCNN achieved 92.81% classification accuracy.
  • Phase Transfer Entropy (PTE) with BrainNetCNN achieved a significantly higher accuracy of 99.50%.
  • PTE demonstrated superior robustness to data artifacts and ability to detect functional connectivity across lags.

Conclusions:

  • The combination of PTE and BrainNetCNN offers a highly accurate and robust method for classifying cognitive workload from EEG data.
  • This approach holds promise for developing advanced passive BCIs for real-time cognitive state monitoring.
  • The findings highlight the potential of directed functional connectivity measures in improving BCI performance.