EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform

Alcoholism, habitual and excessive intake of alcoholic beverages, presents a significant disorder that challenges contemporary society; however, alcoholism detection lacks universally acknowledged examinations or protocols. Traditional subjective methods are time-consuming and prone to error. Electroencephalography (EEG) detects alcoholism by analyzing the electrical activity of the brain. An effective EEG channel selection using metaheuristic algorithms (MHAs)-based features are introduced. The EEG signal is decomposed into subbands using a novel optimal wavelet filter bank (OWFB). Each subband is represented using four features: mean, Higuchi's fractal dimension, log entropy, and Rényi's entropy. The optimal subband features are investigated using combinations of four MHAs and are six classification models. A publicly available 64-channel EEG dataset of alcoholic and non-alcoholic signals is employed. Among the evaluated combinations, Sparrow search algorithm combined with k-nearest neighbor model (KNN) classifier achieved the highest accuracy of 95.90% and F1-score of 96.80%, closely comparable to using all EEG channels (96.30% accuracy and 96.83% F1-score). Overall, KNN classifier consistently outperformed others, indicating that optimal channel selection can effectively reduce channel redundancy while maintaining high accuracy in alcoholism detection. The optimal channel selection enhances the performance of the ML models and reduces the computational time compared to existing alcoholism detection methods. A Python implementation is available at https://github.com/pramodkachare/EEG_Optimal_Wavelet_MHA.