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Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking

Jing Xi1, Xiao-Lin Huang1, Xing-Yan Dang1

  • 1School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China.

Computational and Mathematical Methods in Medicine
|July 8, 2022
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Summary
This summary is machine-generated.

This study demonstrates that network characteristics derived from electroencephalogram (EEG) Gamma rhythms can effectively distinguish memory presence and workload intensity. These findings advance the application of phase-locking value analysis to wide-band EEG signals for memory research.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Electroencephalogram (EEG) is vital for studying working memory, which requires intricate brain region coordination.
  • Previous phase-locking value (PLV) applications were limited to narrow-band signals, with rare successful uses in the wide Gamma rhythm (30-100 Hz) of EEG.

Purpose of the Study:

  • To investigate the utility of EEG-based brain functional networks in assessing working memory.
  • To adapt and validate phase-locking value (PLV) analysis for wide-band Gamma rhythms in EEG.
  • To determine if network characteristics can differentiate memory presence and mental workload intensity.

Main Methods:

  • EEG and behavioral data were collected during memory experiments with varying loads and target forms.
  • EEG data were segmented, and phase-locking value (PLV) of Gamma rhythms between leads was calculated and binarized.
  • Brain functional networks were constructed, and node degree, local clustering coefficient, and betweenness centrality were extracted.
  • Simulations were used to determine the PLV binarizing threshold for wide-band Gamma rhythms.
  • Extracted network characteristics were input into support vector machines (SVM) for classification.

Main Results:

  • Brain functional network characteristics derived from binarized PLV successfully distinguished between the presence and absence of memory.
  • The network characteristics also effectively differentiated the intensity of mental workload during memory tasks.
  • Classification accuracies exceeded 0.78 on an independent test set, demonstrating robust performance.
  • The study successfully adapted PLV analysis for wide-band Gamma rhythms in EEG.

Conclusions:

  • Network characteristics based on binarized PLV are effective indicators of memory presence and mental workload intensity.
  • This research validates a novel approach for phase-locking investigation in wide-band signals like EEG Gamma rhythms.
  • The findings offer new insights into memory research using EEG and advanced network analysis techniques.