Taiwanese Researchers Use EEG to Identify Internet Addiction with High Accuracy

A research team from Taiwan has created a machine-learning model capable of analyzing electroencephalography (EEG) brain wave patterns to identify individuals suffering from Internet addiction. This innovative approach boasts an impressive accuracy rate of 86 percent, surpassing traditional self-reported measures, according to findings released yesterday.

The study, led by Huang Hsu-wen, an assistant investigator at the National Health Research Institutes’ National Center for Geriatrics and Welfare Research, examined the brain activity of 92 participants—42 with Internet addiction and 50 healthy controls. The research revealed that those with addiction displayed significantly elevated levels of phase synchronization in their resting-state EEG functional connectivity.

Huang explained during a news conference that these alterations in brain activity are indicative of disrupted neural systems, particularly within the inhibitory and reward pathways. Notably, the EEG changes occur prior to the manifestation of addictive behaviors, suggesting that this method could facilitate earlier identification of individuals at risk for Internet addiction.

The implications of this research extend beyond academic interest. By combining EEG testing with machine-learning classification models, schools and healthcare institutions could intervene more effectively, targeting at-risk individuals before addiction behaviors develop.

Internet addiction, characterized by an inability to limit online engagement and feelings of discomfort when disconnected from the Internet, has become a growing concern in today’s digital age. The findings from this study were published in May 2023 in the journal Psychological Medicine.

Other key contributors to the research include Wu Shun-chi, a professor in the Department of Engineering and System Science at National Tsing Hua University, and Huang Chih-mao, an associate professor in the Department of Psychology at the University of Hong Kong. Their collaboration underscores the importance of interdisciplinary efforts in addressing complex health issues.

As technology continues to shape daily life, understanding and addressing Internet addiction through scientific methods like this offers a promising avenue for future research and intervention.