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Cortical Complexity Alterations in Methamphetamine, Cannabis, and Opioid Users: An EEG-Based Analysis
Nasimeh Marvi1, Javad Haddadnia2, Mohammad Reza Fayyazi Bordbar3
1Department of Electrical Engineering Khorasan Institute of Higher Education, Mashhad, Iran.
Journal of Biomedical Physics & Engineering
|August 12, 2026
Summary
This study developed an automatic system using brainwave analysis to detect substance abuse, achieving high accuracy in identifying users of Methamphetamine, Cannabis, and Opioids.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Data Science
Background:
- Drug abuse poses significant health risks, necessitating advanced diagnostic tools.
- Current diagnostic methods for substance abuse require improvement.
- Identifying specific substance use patterns is crucial for effective treatment.
Purpose of the Study:
- To create an automated system for detecting substance abuse.
- To specifically identify users of Methamphetamine (Meth), Cannabis (Can), and Opioids (Op).
- To analyze changes in brain complexity associated with different substance use.
Main Methods:
- Nonlinear Electroencephalogram (EEG) signal analysis.
- Support Vector Machine (SVM) classification.
- Recurrence Quantification Analysis (RQA) to extract determinism and complexity parameters.
Main Results:
- EEG complexity decreased in Opioid and Methamphetamine users, potentially indicating reduced cognitive complexity.
- EEG complexity increased in Cannabis users compared to healthy controls.
- The classification system achieved 88.77% accuracy, 87.69% sensitivity, and 96.30% specificity.
Conclusions:
- An automated diagnostic assistance system using nonlinear brain data analysis was successfully developed.
- The system can effectively differentiate Methamphetamine, Opioid, and Cannabis users from healthy individuals.
- This approach offers a promising tool for objective substance abuse diagnosis.
