Empirical wavelet transform based automated alcoholism detecting using EEG signal features.

•Proposed empirical wavelet transform (EWT) based automated classification model for alcoholism detection.•Feature vectors are extracted from EEG signals using Hilbert–Huang transform (HHT).•Improved Time- frequency representation using HHT.•Classifiers parameter optimization is performed to improved the classification performance.•The proposed model achieved 98.76 % average accuracy and 98 % AUC value with LS-SVM (polynomial kernel) learner.