Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity
Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.
Authors
- Feng‐Ying Huang (ORCID: https://orcid.org/0000-0001-9677-6855)
- Chih‐Mao Huang (ORCID: https://orcid.org/0000-0002-6209-2575)
- You-Xun Chang
- Po-Wei Chen (ORCID: https://orcid.org/0009-0003-0597-1201)
- M H Chen (ORCID: https://orcid.org/0000-0003-1385-6206)
- Qiduo Lin (ORCID: https://orcid.org/0000-0002-4040-4101)
- Chihling Liu (ORCID: https://orcid.org/0000-0002-8290-1261)
- Shun‐Chi Wu (ORCID: https://orcid.org/0000-0001-9337-2927)
- Peixun Li (ORCID: https://orcid.org/0000-0002-7749-1472)
- Chemin Lin (ORCID: https://orcid.org/0000-0001-6889-4686)
Institutions
- National Yang Ming Chiao Tung University (TW)
- National Health Research Institutes (TW)
- City University of Hong Kong (HK)
- Chang Gung University (TW)
- National Tsing Hua University (TW)
- Keelung Chang Gung Memorial Hospital (TW)
Publication Details
- Journal
- Psychological Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/s0033291725001035
- Citations
- 5
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 7.16
Funders
- City University of Hong Kong