Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network

摘要

Alcoholism is a disorder characterized by excessive consumption and dependence on alcohol. There are various ways to detect whether a patient is addicted to alcohol, one of them by brain detection using electroencephalograph (EEG). However, the signals generated by the EEG recorder should be prepared to do further processing to detect brain abnormalities automatically. Therefore, this research implements Wavelet Packet Decomposition (WPD) method for feature extraction, Principal Component Analysis (PCA) for dimension reduction, and Back Propagation Neural Network optimized with Genetic Algorithm for alcohol addiction classification. Based on the experiment results, the best performance was 94.00% accuracy with decomposition of 3 levels, taking 30% of the features, and classification using Neural Network and Genetic Algorithm with learning rate of 0.1.