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Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning

Riaz Muhammad1, Ezekiel Edward Nettey-Oppong1, Muhammad Usman2

  • 1Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.

Bioengineering (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study found unique brainwave patterns in individuals with Gaming Disorder (GD) during gameplay. Occipital EEG biomarkers show potential for objective screening of this behavioral addiction.

Keywords:
gaming disordermachine learningoccipital cortexspectral slowingtask-based EEG

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Area of Science:

  • Neuroscience
  • Behavioral Addiction Research
  • Computational Psychiatry

Background:

  • Gaming Disorder (GD) is a recognized behavioral addiction with known resting-state impairments.
  • Neurophysiological dynamics during active gameplay in GD remain largely unexplored.
  • Understanding task-based neural activity is crucial for developing objective diagnostic tools.

Purpose of the Study:

  • To identify task-based occipital electroencephalography (EEG) biomarkers specific to Gaming Disorder.
  • To assess the diagnostic utility of these EEG biomarkers for GD detection.
  • To explore the neurophysiological differences between individuals with and without GD during active gaming.

Main Methods:

  • Collected occipital EEG (O1/O2) data from 30 participants (15 with GD, 15 controls) during mobile gaming.
  • Extracted spectral, temporal, and nonlinear complexity features from EEG data.
  • Utilized Random Forest for feature relevance ranking and Leave-One-Subject-Out cross-validation with five machine learning models for classification.

Main Results:

  • The Gaming Disorder group exhibited 'spectral slowing' during gameplay: increased Delta/Theta power and decreased Beta activity.
  • Beta variability emerged as a key biomarker, indicating altered attentional stability in GD.
  • A Decision Tree classifier achieved 80.0% accuracy in distinguishing between GD and control groups.

Conclusions:

  • Distinct neurophysiological patterns, including increased low-frequency power, are associated with Gaming Disorder during gameplay.
  • Occipital EEG biomarkers, particularly Beta variability, show promise as objective screening metrics for GD.
  • These findings suggest potential 'neural efficiency' or automatized processing in GD despite active task engagement.