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Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation
Published on: June 17, 2013
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A novel machine learning-based method to quantify the effect of transcranial direct current stimulation on opioid
Fatemeh Kazemzadeh1, Sepideh Jabbari1, Bahram Perseh2
1Department of Electrical Engineering, University of Zanjan, Zanjan, Iran.
The International Journal of Neuroscience
|October 1, 2025
Summary
Electroencephalography (EEG) and transcranial direct current stimulation (tDCS) show promise for diagnosing opioid addiction. This combined approach effectively reduced craving in patients undergoing treatment.
Area of Science:
- Neuroscience
- Addiction Medicine
- Biomedical Engineering
Background:
- Opioid addiction presents significant public health challenges.
- Current diagnostic methods for addiction have limitations.
- Novel diagnostic and therapeutic strategies are needed.
Purpose of the Study:
- To investigate the efficacy of electroencephalography (EEG) combined with transcranial direct current stimulation (tDCS) for diagnosing and treating opioid addiction.
- To develop a machine learning algorithm for accurate addiction diagnosis using EEG data.
- To assess the impact of tDCS on reducing craving in opioid-addicted individuals.
Main Methods:
- Thirty-six male patients on methadone maintenance were divided into three groups: left anodal/right cathodal tDCS, right anodal/left cathodal tDCS, and sham stimulation.
- EEG recordings were collected before and after tDCS, alongside data from 24 healthy controls.
- Machine learning was employed to analyze EEG channels for distinguishing addicted individuals from controls.
Main Results:
- The developed algorithm achieved a diagnostic accuracy of 94.30% for opioid addiction.
- Both active tDCS groups (A and B) showed significant reductions in craving levels.
- EEG signals, questionnaires, and blood biomarkers corroborated the craving reduction findings.
Conclusions:
- Transcranial direct current stimulation (tDCS) is a potentially effective intervention for reducing craving in patients with opioid addiction.
- The combination of EEG and machine learning offers a promising avenue for addiction diagnosis.
- This study highlights a novel, integrated approach for managing opioid addiction.
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