Identifying Methamphetamine Users through EEG Analysis: Harnessing hctsa and Machine Learning Approaches

Abstract

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 techniques reached 100% accuracy in 134, 89, and 100 states respectively. The inclusion of four-feature combinations further increased these numbers, with SVM, LR, and RF achieving 100% accuracy in 2933, 3109, and 589 states respectively. Notably, only LR achieved 100% accuracy when using all 40 features. This study demonstrated that SVM, LR, and RF classifiers combined with feature extraction through the hctsa method exhibit an exceptional capacity to accurately identify methamphetamine users among healthy individuals using a single EEG channel with a classification accuracy of up to 100%.