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Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.

Kaiwei Yu1, Jiafa Chen2, Xian Ding1

  • 1Research Center of Optical Instrument and System, Ministry of Education and Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai, 200093, China.

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Summary

This study integrates functional near-infrared spectroscopy (fNIRS) and eye tracking to accurately classify cognitive load. Combining these methods enhances neurocognitive analysis for improved cognitive science research.

Keywords:
Eye trackingMachine learningNumber of featuresfNIRSmRMR

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Biomedical Engineering

Background:

  • Cognitive load classification is vital for understanding psychological processes.
  • Single-modality approaches face limitations in feature selection and data dimensionality.

Purpose of the Study:

  • To innovatively combine functional near-infrared spectroscopy (fNIRS) and eye tracking for neurocognitive classification of cognitive load.
  • To overcome limitations of single-modality techniques and address challenges in feature selection and data analysis.

Main Methods:

  • Collected fNIRS and eye tracking data during cognitive tasks.
  • Applied the maximum relevance minimum redundancy algorithm for feature extraction.
  • Utilized machine learning models (naive Bayes, SVM, KNN, random forest) with cross-validation for performance evaluation.

Main Results:

  • Demonstrated the effectiveness of fNIRS-eye tracking integration in discriminating cognitive load levels.
  • Highlighted the significant impact of feature set size on classification performance.
  • Validated the utility of the maximum relevance minimum redundancy algorithm and machine learning techniques.

Conclusions:

  • The combined fNIRS-eye tracking approach offers a robust method for cognitive load classification.
  • Optimizing feature selection is crucial for enhancing classification accuracy.
  • Findings advance neuroscientific understanding of cognitive load and neural psychology.