Taiwanese Researchers Develop EEG Model to Identify Internet Addiction

A research team from Taiwan has introduced an innovative machine-learning model that utilizes electroencephalography (EEG) to identify individuals suffering from Internet addiction. This model distinguishes those with the condition from healthy individuals with an impressive accuracy rate of 86 percent, according to findings released on March 14, 2024.

The study, which is significant for its potential applications in early detection, involved 92 participants, including 42 individuals diagnosed with Internet addiction and 50 healthy controls. The research team analyzed the resting-state EEG functional connectivity of the participants. They discovered that those with Internet addiction exhibited increased levels of phase synchronization between brain regions, a finding that indicates the condition disrupts neural systems related to inhibition and reward pathways.

Huang Hsu-wen, an assistant investigator at the National Health Research Institutes’ National Center for Geriatrics and Welfare Research, played a pivotal role in this research. During a news conference, she emphasized that the alterations in EEG patterns occur prior to the manifestation of addictive behaviors. This advancement suggests that EEG testing, when combined with machine-learning classification models, could effectively identify early risk signals associated with Internet addiction. Such early identification would allow schools and medical institutions to implement targeted interventions with greater precision.

Internet addiction is characterized by excessive online engagement, an inability to reduce the urge to go online, and discomfort when disconnected, as outlined in the study published in the journal Psychological Medicine in May 2023. Huang noted that the model offers a more accurate alternative to self-reported measures, which often lack reliability.

The research team also included notable contributors such as 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 collaborative efforts, along with other institutions in Taiwan and internationally, mark a significant step forward in understanding and addressing Internet addiction.

As Internet usage continues to rise globally, the implications of this research extend beyond academic interest. Early detection and intervention strategies could prove vital in mitigating the adverse effects of Internet addiction on individuals’ mental health and overall well-being. The findings from this study may pave the way for new diagnostic protocols and treatment options for those affected by this increasingly prevalent condition.