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Analyzing EEG data during opium addiction treatment using a fuzzy logic-based machine learning model.

Elnaz DehAbadi1, Fateme Ayşin Anka2, Fateme Vafaei3,4

  • 1Garmsar Branch, Islamic Azad University, Garmsar, Iran.

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|November 19, 2025
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Summary

Electroencephalography (EEG) complexity measures show promise for diagnosing opium addiction. Machine learning models effectively identified neural complexity changes linked to addiction stages, aiding treatment assessment.

Keywords:
EEG data analysisfuzzy logicneural activity patternsopium addictionsubstance abuse treatment

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Assessing substance abuse treatment and predicting outcomes noninvasively is challenging.
  • Electroencephalography (EEG)-derived complexity measures may offer insights into clinical diagnosis of addiction.
  • Opium addiction's neural underpinnings require further investigation for effective treatment monitoring.

Purpose of the Study:

  • To investigate psychological and neural complexity differences in male opium addicts across various treatment stages.
  • To develop and validate a machine learning (ML) model using fuzzy logic to analyze EEG data for addiction assessment.
  • To identify neural complexity changes associated with opium addiction and treatment using EEG.

Main Methods:

  • Categorized male participants into four groups: active addicts, short-term treatment, long-term treatment, and healthy controls.
  • Utilized psychological assessments and collected EEG data, analyzing neural complexity with Higuchi Fractal Dimension (HFD).
  • Applied ML classifiers and feature selection to EEG data to distinguish addiction stages.

Main Results:

  • Observed significant distress levels and poorer general health in addicts, with improvements post-treatment.
  • Identified significant differences in neural complexity in brain regions associated with attention, memory, and executive functions.
  • The developed ML model successfully classified addiction stages based on EEG-derived neural complexity features.

Conclusions:

  • Machine learning and fuzzy logic show potential for assessing addiction-related neural dynamics in opioid addiction.
  • EEG-derived neural complexity biomarkers offer promise for personalized addiction diagnosis and treatment monitoring.
  • Findings contribute to understanding the pathophysiology of opium addiction and inform noninvasive assessment strategies.