Explaining electroencephalogram channel and subband sensitivity for alcoholism detection

Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection can aid in early interventions and timely corrective actions. For this purpose, electroencephalogram (EEG) signals are investigated using explainable artificial intelligence (XAI) techniques to obtain biomarkers. EEG signals are decomposed into five frequency bands to generate vectors of band powers of all channels. These vectors are input to different machine learning models and their ensembles to detect alcoholism. The reliability and generalization of these models are investigated using three oversampling techniques: Synthetic minority over-sampling technique, Adaptive Synthetic Sampling data augmentation, and Gaussian. Comparative analysis using an open-source EEG dataset showed superior performance for artificial neural network (ANN), with an accuracy of 97.36% and an F1-score of 97.88%. The oversampling techniques further improved performance across various ML models, and SMOTE-based ANN outperformed with an accuracy of 97.93% and an F1-score of 97.99%. The best ANN model is investigated using three XAI techniques, Local Interpretable Model-agnostic Explanations (LIME), Submodular Pick LIME, and Morris sensitivity analysis, for explaining the sensitivity of EEG channels and bands in alcoholism detection. The frequency band explanations showed relatively higher performance using beta and gamma bands with 95.45% and 97.36% accuracy, respectively. The EEG channel explanations showed higher performance using biomarkers from parietal and central regions, providing 95.41% and 91.50% accuracy, respectively. A combined explanation of all three XAI techniques indicated that three from the parietal and three from the central regions are the most important for improving detection performance. This study validates the effectiveness of the ANN in detecting alcoholism using EEG signals and emphasizes the significance of frequency bands and EEG channels. These explanations could be applied for early detection and monitoring.