Evaluation of Baseline Craving in Patients with Methamphetamine Addiction Using EEG Signals in Neurofeedback Process

Zolfagharzadeh Kermani, Mahdi; Khalilzadeh, Mohammad Ali; Ghoshuni, Majid; Hashemian, Peyman

doi:10.22041/ijbme.2016.22212

Document Type : Full Research Paper

Authors

¹ Master,Department of Biomedical engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

² Associate Professor, Department of Biomedical engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

³ Assistant Professor, Department of Biomedical engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

⁴ Assistant Professor, MashhadUniversity of Medical Sciences, Department of Psychiatry, Mashhad, Iran

10.22041/ijbme.2016.22212

Abstract

Evaluation and measurement of parameters associated with methamphetamine craving can be a valuable tool in the management and intervention programs related to methamphetamine use and dependence. We believe that quantitative electroencephalography (EEG) have brought about a revolution in identification the neurologic infrastructure of craving processing. This study has been conducted aimed to design and develop a new method to measure baseline craving in methamphetamine-dependent patients using EEG signals in neurofeedback therapy for separation of the three modes of low, medium, and high craving. For this purpose, 10 methamphetamine abusers were selected by available method in terms of age, sex and IQ. All patients received 10 sessions of neurofeedback therapy with alpha-theta protocol. During the period of treatment with neurofeedback, before and 60 minutes after each training session, at rest state, on Pz, for 2 minutes and 10 seconds EEG was recorded. To labeling EEG signals we have used Desire for Drug Questionnaire (DDQ). After collecting the required data from signals, time, frequency and nonlinear features were extracted. Then by calculating the linear correlation coefficient of the two variables and variance analysis on three levels optimized and effective features were selected. Finally, using fuzzy classifier, those features were separated into three classes of low, medium and high craving. According to the results, separation accuracy of EEG signals in three classes by K-fold Cross-Validation (KCV)method was 96.67% and test data was 75.15%. This study showed in addition to estimating baseline craving in methamphetamine abusers by quantifying EEG we can optimize the number of training sessions for participants.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Evaluation of Baseline Craving in Patients with Methamphetamine Addiction Using EEG Signals in Neurofeedback Process

References

References

Volume 9, Issue 3
December 2015
Pages 243-251

Evaluation of Baseline Craving in Patients with Methamphetamine Addiction Using EEG Signals in Neurofeedback Process

References

References

Volume 9, Issue 3December 2015Pages 243-251

Volume 9, Issue 3
December 2015
Pages 243-251