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Identifying Methamphetamine Users through EEG Analysis: Harnessing hctsa and Machine Learning Approaches by Reza Meynaghizadeh-Zargar; Sareh Kazmi; Saeed Sadigh-Eteghad; Abdollah Barati; Ali Reza Shafiee-Kandjani is a scholarly article available to read on EtoBox.

What is Identifying Methamphetamine Users through EEG Analysis: Harnessing hctsa and Machine Learning Approaches about?

## Abstract The objective of this study was to evaluate the potential of accurately distinguishing methamphetamine users from a cohort of healthy individuals by analyzing electroencephalography (EEG) signals and utilizing machine learning techniques. Ten participants with methamphetamine dependence and nine healthy individuals were subjected to a 19-channel EEG recording. A highly comparative time series analysis (hctsa) method was employed for feature extraction from the EEG signals. Subsequently, three machine learning techniques, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), were implemented to process the data. A nine-fold cross-validation approach was utilized to prevent overfitting during the training process. Using the hctsa method, 6,070 features were extracted while discarding 1,682 erroneous or valueless data points. Forty informative features were selected for machine learning implementation. Although single features did not achieve 100% accuracy, combinations of two features resulted in two distinct states predicting values with 100% accuracy when employing the SVM approach. With three-feature combinations, SVM, LR, and RF techni

Author

Reza Meynaghizadeh-Zargar; Sareh Kazmi; Saeed Sadigh-Eteghad; Abdollah Barati; Ali Reza Shafiee-Kandjani

Publisher

Research Square Platform LLC

Published

2023

Language

EN