Novel AUD Likelihood detection based on EEG Classification

This paper addresses a novel machine learning approach to classify EEG signals that can be useful for discovering likelihood of suffering from genetic alcoholism. Excessive consumption of alcohol can create genetic cognitive impairment and contribute to alcohol disorder in descendants. Children can inherit it either directly from parents or any ancestors. Revealing chances of AUD would help in deciding preventive intervention efficiently. Major contribution of this work is classification based on input augmentation and automatic feature extraction. Instead of using signal from many spatial nodes proposed classification task relies on EEG signal from only two electrodes, therefore it is computationally efficient. Results are shown for performance comparison between customary and augmented EEG as inputs to the classifier. Input augmentation is confirmed for 15% improvement in accuracy.