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Related Experiment Video

Updated: May 7, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Multi-level cognitive state classification of learners using complex brain networks and interpretable machine

Xiuling He1,2, Yue Li1,2, Xiong Xiao1,2

  • 1National Engineering Research Center of Educational Big Data, Central China Normal University, Luoyu Road, Wuhan, 430079 Hubei China.

Cognitive Neurodynamics
|January 6, 2025
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Summary

This study used electroencephalography (EEG) to analyze brain networks during learning, identifying cognitive states with 88.07% accuracy using eXtreme Gradient Boosting (XGBoost). Findings highlight frontal, temporal, and central brain connections crucial for higher-order thinking skills (HOTS).

Keywords:
Cognitive stateElectroencephalography (EEG)Functional brain networkPhase locking valueSHapley Additive exPlanations (SHAP)

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Area of Science:

  • Cognitive Neuroscience
  • Educational Psychology
  • Machine Learning

Background:

  • Understanding learner cognitive states is vital for developing higher-order thinking skills (HOTS).
  • Previous electroencephalography (EEG) studies often focused on single channels, neglecting inter-channel connectivity.
  • There's a need for advanced methods to analyze complex brain dynamics during diverse learning activities.

Purpose of the Study:

  • To investigate whole-brain network dynamics during distinct learning activities using EEG.
  • To classify cognitive states based on functional brain network characteristics.
  • To identify key brain regions and connections associated with higher cognitive states.

Main Methods:

  • Designed three learning activities (video watching, keyword extraction, essay creation) based on Bloom's taxonomy and ICAP framework.
  • Recorded EEG signals from 31 college students during these activities.
  • Applied EEG microstate sequence analysis, phase locking value for network construction, and machine learning classifiers (SVM, KNN, Random Forest, XGBoost).

Main Results:

  • eXtreme Gradient Boosting (XGBoost) achieved the highest accuracy (88.07%) in classifying cognitive states.
  • EEG microstate sequence analysis revealed dynamic changes in brain networks corresponding to learning activities.
  • SHapley Additive exPlanations (SHAP) identified frontal, temporal, and central brain region connections as critical for high cognitive states.

Conclusions:

  • The study successfully classified cognitive states using EEG-based functional brain network analysis.
  • XGBoost and SHAP analysis provide effective tools for understanding brain dynamics related to learning.
  • Findings support the design of cognitive-guided instructional strategies to enhance learners' HOTS.