ADHD detection from EEG signals using GCN based on multi-domain features
The fused functional connectivity matrix surpasses traditional single-metric approaches in characterizing brain interactions. By synergistically combining time, frequency, and topological features, the GCN framework enables more precise ADHD detection. This method demonstrates potential for assisting neurologists in clinical diagnosis while providing interpretable neurophysiological insights.
Authors
- Yanyan Chen (ORCID: https://orcid.org/0000-0002-2898-6700)
- Xiaofeng Guo (ORCID: https://orcid.org/0000-0002-7258-3060)
- Ling Li (ORCID: https://orcid.org/0000-0001-9722-9503)
- Zihan Yang (ORCID: https://orcid.org/0009-0007-4172-9673)
- Xirui Peng (ORCID: https://orcid.org/0000-0001-6654-3831)
- Xu Liu (ORCID: https://orcid.org/0000-0001-9916-0968)
- Lina Han
- Yanping Zhao (ORCID: https://orcid.org/0009-0003-4379-0735)
- Junxian Yang (ORCID: https://orcid.org/0009-0005-5965-7167)
Institutions
- Jilin University (CN)
- Jilin Province Science and Technology Department (CN)
- Sixth Hospital of Changchun City (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-04-04
- DOI
- https://doi.org/10.3389/fnins.2025.1561994
- Citations
- 7
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 10.02
Funders
- People's Government of Jilin Province
- Jilin Scientific and Technological Development Program