Analyzing EEG data during opium addiction treatment using a fuzzy logic-based machine learning model
This study demonstrates the potential of ML and fuzzy logic in assessing addiction-related neural dynamics, offering insights into opioid addiction's pathophysiology. The findings highlight the promise of brainwave-based biomarkers for personalized addiction diagnosis and treatment monitoring.
Authors
- Peyman Hassani‐Abharian (ORCID: https://orcid.org/0000-0003-4683-066X)
- Farzad Kiani (ORCID: https://orcid.org/0000-0002-0354-9344)
- Sajjad Nematzadeh (ORCID: https://orcid.org/0000-0001-5064-2181)
- Mahsa Torkamanian-Afshar
- Hossein Lanjanian (ORCID: https://orcid.org/0000-0003-4284-6592)
- Fateme Ayşin Anka
- Elnaz DehAbadi
- Fateme Vafaei
- Nazanin Aghahosseinzargar
Institutions
- Islamic Azad University of Garmsar (IR)
- Shahed University (IR)
- Institute for Cognitive Science Studies (IR)
- Basir Eye Health Research Center (IR)
- Research Institute for Endocrine Sciences (IR)
- Istinye University (TR)
- Fatih Sultan Mehmet Waqf University (TR)
- Istanbul University (TR)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-11-03
- DOI
- https://doi.org/10.3389/fpsyt.2025.1635933
- Citations
- 1
- Primary Topic
- Neurotransmitter Receptor Influence on Behavior
- Type
- article
- Field-Weighted Citation Impact
- 0.91