Classification of EEG-Based Auditory Evoked Potentials Using Entropy-Based Features and Machine Learning Techniques

Abstract

Hearing loss is a prevalent impairment that disrupts interactions with others and individuals' learning abilities. Immediate and accurate diagnosis of hearing loss using Electroencephalogram (EEG) signals, particularly Auditory Evoked Potentials (AEP), is considered the most effective approach to address this issue. The AEP signals, generated in the cerebral cortex in response to auditory stimuli, serve as the most reliable method for diagnosing deafness. This study introduces a novel approach for detecting hearing ability through the classification of EEG-AEP signals. The current experiment makes use of a publicly available dataset that contains AEP responses from 16 people who responded to auditory stimuli on either the left or right side. Sample Entropy is employed to extract the feature, capturing the complex temporal dynamics of the EEG signals. Four popular machine learning-based classifiers, namely Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR), are utilized for classification purposes. The results indicate that SVM achieves the highest classification accuracy of 99.37% with subject-4 and the average accuracy of 90.74% is achieved with all subjects. This finding shows the effectiveness of Sample Entropy as a feature extraction technique for characterizing AEPs and highlights the potential of SVM as a robust classifier for the accurate identification of auditory stimuli localization. The accuracy achieved in this study indicates a promising direction for the development of reliable and non-invasive methods for hearing-related diagnoses.