Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.

Journal: Psychological medicine
PMID: 40376927

Abstract

BACKGROUND: Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.

Authors

  • Hsu-Wen Huang
    National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan.
  • Po-Yu Li
    FullHope Biomedical Co., Ltd, New Taipei City, Taiwan.
  • Meng-Cin Chen
    Department of Eengineering and System Science, National Tsing Hua University, Hsinchu, Taiwan.
  • You-Xun Chang
    Department of Eengineering and System Science, National Tsing Hua University, Hsinchu, Taiwan.
  • Chih-Ling Liu
    Department of Eengineering and System Science, National Tsing Hua University, Hsinchu, Taiwan.
  • Po-Wei Chen
    Department of Eengineering and System Science, National Tsing Hua University, Hsinchu, Taiwan.
  • Qiduo Lin
    Department of Linguistics and Translation, City University of Hong Kong, Hong Kong.
  • Chemin Lin
  • Chih-Mao Huang
  • Shun-Chi Wu

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