Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning
These results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.
Authors
- Abdul Halim Abdul Latiff (ORCID: https://orcid.org/0000-0001-8594-6767)
- Norashikin Yahya (ORCID: https://orcid.org/0000-0002-9812-0435)
- Nasreen Badruddin (ORCID: https://orcid.org/0000-0002-5666-3984)
- Mohammed J. Abdulaal (ORCID: https://orcid.org/0000-0003-1734-8399)
- Danish M. Khan (ORCID: https://orcid.org/0000-0001-8815-6146)
- Ainul Khairiyah Mohd Nazri
- Noor'Izni Zafirah Mohd Radzi
Institutions
- Petronas (Malaysia) (MY)
- King Abdulaziz University (SA)
- Universiti Teknologi Petronas (MY)
- Sunway University (MY)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2025-01-10
- DOI
- https://doi.org/10.3389/fnins.2024.1524513
- Citations
- 4
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- King Abdulaziz University