A Taiwanese research team has developed a machine-learning model that analyzes electroencephalography (EEG) brain wave patterns to distinguish individuals with Internet addiction from healthy subjects with 86 percent accuracy, researchers said yesterday.
The method’s accuracy is significantly higher than that of self-reported measures, said Huang Hsu-wen (黃緒文), an assistant investigator at the National Health Research Institutes’ National Center for Geriatrics and Welfare Research and one of the study’s lead researchers.
After analyzing 92 participants’ (42 with Internet addiction and 50 healthy controls) resting-state EEG functional connectivity, the researchers found the addicted group showed elevated levels of phase synchronization, Huang told a news conference.
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She said she believed it was because addiction disrupted neural systems in the inhibitory and reward pathways.
The changes in EEG patterns occur before addictive behaviors manifest, meaning that EEG testing combined with machine-learning classification models could identify early risk signals more efficiently and enable schools and medical institutions to intervene with greater precision, she said.
Internet addiction refers to prolonged online engagement, inability to curb the urge to go online and discomfort when disconnected from the Internet, according to the study, which was published in May in the journal Psychological Medicine.
Other contributors to the research included Wu Shun-chi (吳順吉), a professor in National Tsing Hua University’s Department of Engineering and System Science; Huang Chih-mao (黃植懋), an associate professor in the University of Hong Kong’s Department of Psychology; and research institutions in Taiwan and overseas.