Authors - Shipra Swati, Sunita Kumari, Santwana Sneha Abstract - The significant changes in brain dynamics caused by alcohol addiction can be captured by electroencephalography (EEG). Automated alcoholism detection using EEG has gained attention as a non-invasive, objective replace traditional clinical assessments. This study provides a detailed comparison between conventional machine learning models and deep learning architectures for the EEG-based classification of alcoholism. It uses a publicly available multichannel EEG dataset containing recordings of both control and alcoholic subjects. Preprocessing and feature extraction in the time, frequency, and time-frequency domains are done before the assessment of traditional classifiers like k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). Furthermore, image-like EEG representations were used to adapt deep convolutional neural networks (ResNet and GoogleLeNet) for classification. According to experimental results, KNN achieves competitive accuracy with little training time, while ensemble methods and deep residual networks perform better than simpler classifiers. The results demonstrate the relative benefits and drawbacks of deep learning and statistical learning paradigms for EEG-based alcoholism detection.
