Taipei: 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 announced Thursday. The method’s accuracy is significantly higher than that of self-report measures, Huang Hsu-wen, an assistant investigator at the National Health Research Institutes’ National Center for Geriatrics and Welfare Research and one of the study’s lead researchers, told a press event.

According to Focus Taiwan, after analyzing 92 participants’ (including 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 explained that addiction might disrupt neural systems in the inhibitory and reward pathways. She further mentioned that changes in EEG patterns occur before addictive behaviors manifest, indicating that EEG testing combined with machine-learning classification models could identify early risk signals more efficiently. This advancement could enable schools and medical institutions to intervene with greater precision.

Internet addiction is characterized by prolonged online engagement, an inability to curb the urge to go online, and discomfort when disconnected from the internet, as stated in the research article. The article was published in Psychological Medicine, an international medical journal, in May 2025.

Other contributors to the research included Wu Shun-chi, a professor in the Department of Engineering and System Science at National Tsing Hua University; Huang Chih-mao, an associate professor in the Department of Psychology at the University of Hong Kong; as well as research institutions in Taiwan and overseas.