Identifying Methamphetamine Users Through Electroencephalography Analysis: Harnessing Highly Comparative Time-Series Analysis and Machine Learning Approaches

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Background: Methamphetamine is a chemical substance which affects the brain electrical activity of addicted individuals. The study aimed to assess the potential of using electroencephalography (EEG) signals and machine learning (ML) techniques to distinguish individuals with methamphetamine (MA) dependence from healthy individuals. Methods: The researchers utilized highly comparative time-series analysis (hctsa) for feature extraction. Three ML algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), were employed to process the data. Various combinations of top 40 features were used to test the possibility of reaching 100% accuracy. Results: Although individual features did not achieve 100% accuracy, combinations of two features resulted in two distinct states with a prediction accuracy of 100% when using the SVM approach. Even more combinations of features with 100% accuracy were found when utilizing more features. Conclusion: Based on the findings, SVM, LR, and RF classifiers, combined with feature extraction through the hctsa method, demonstrated exceptional accuracy in identifying MA users among healthy individuals using a single EEG channel. The classification accuracy reached up to 100%.