Taiwanese Researchers Use EEG to Identify Internet Addiction

A research team from Taiwan has developed a groundbreaking machine-learning model capable of analyzing electroencephalography (EEG) brain wave patterns to identify individuals with Internet addiction. The model achieves an impressive accuracy rate of 86 percent in distinguishing these individuals from healthy subjects, according to findings presented at a recent news conference.

The study, led by Huang Hsu-wen, an assistant investigator at the National Health Research Institutes’ National Center for Geriatrics and Welfare Research, highlights the limitations of traditional self-reported measures of addiction. Huang noted that the new method’s accuracy significantly surpasses those self-reported assessments, providing a more reliable means of diagnosis.

Research Findings and Implications

In the study, the team analyzed resting-state EEG functional connectivity in 92 participants, including 42 diagnosed with Internet addiction and 50 healthy controls. The results revealed that individuals with Internet addiction exhibited higher levels of phase synchronization in their brain activity. Huang explained that this increased synchronization likely results from the disruption of neural systems involved in inhibitory and reward pathways.

Importantly, the observed changes in EEG patterns occur before addictive behaviors manifest. This suggests that EEG testing, when combined with machine-learning classification models, could effectively identify early warning signs of addiction. Such early detection would allow schools and medical institutions to implement timely interventions, potentially improving outcomes for those at risk.

According to the study, published in May 2023 in the journal Psychological Medicine, Internet addiction is characterized by prolonged online engagement, an inability to resist the urge to go online, and feelings of discomfort when disconnected from the Internet. This condition has become increasingly relevant in today’s digital age, as more individuals experience challenges related to excessive Internet use.

The research also featured contributions from Wu Shun-chi, a professor at National Tsing Hua University, and Huang Chih-mao, an associate professor at the University of Hong Kong. Their collaborative effort underscores the importance of interdisciplinary approaches in addressing complex issues like Internet addiction.

This innovative methodology offers hope for developing targeted strategies to combat Internet addiction and help individuals regain control over their online habits. As technology continues to evolve, understanding its impact on mental health will be crucial for fostering healthier digital environments.