Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose challenges in processing incoming auditory stimulus that can be measured using electroencephalography (EEG). The electrophysiological behaviour response emanated from EEG auditory evoked potential (AEP) requires highly trained professionals for analysis and interpretation. Reliable automated methods using techniques of machine learning would assist the auditory assessment process for informed treatment and practice. It is thus highly required to develop models that are more efficient and precise by considering the characteristics of brain signals. This study aims to provide a comprehensive review of several state-of-the-art techniques of machine learning that adopt EEG evoked response for the auditory assessment within the last 13 years. Out of 161 initially screened articles, 11 were retained for synthesis. The outcome of the review presented that the Support Vector Machine (SVM) classifier outperformed with over 80% accuracy metric and was recognized as the best suited model within the field of auditory research. This paper discussed the comprehensive iterative properties of the proposed computed algorithms and the feasible future direction in hearing impaired rehabilitation.

听力损失是一种常见的神经退行性疾病，可以在生命的任何阶段开始。听觉神经损伤的错位可能会给处理传入的听觉刺激带来挑战，这些刺激可以使用脑电图 (EEG) 进行测量。EEG 听觉诱发电位 (AEP) 发出的电生理行为反应需要训练有素的专业人员进行分析和解释。使用机器学习技术的可靠自动化方法将有助于听觉评估过程，以进行知情治疗和实践。因此，非常需要通过考虑大脑信号的特征来开发更有效和更精确的模型。本研究旨在全面回顾过去 13 年来采用 EEG 诱发反应进行听觉评估的几种最先进的机器学习技术。在最初筛选的 161 篇文章中，有 11 篇被保留用于合成。审查结果表明，支持向量机 (SVM) 分类器的性能优于 80% 的准确度指标，并被公认为听觉研究领域中最适合的模型。本文讨论了所提出的计算算法的综合迭代特性以及听力受损康复的可行未来方向。审查结果表明，支持向量机 (SVM) 分类器的性能优于 80% 的准确度指标，并被公认为听觉研究领域中最适合的模型。本文讨论了所提出的计算算法的综合迭代特性以及听力受损康复的可行未来方向。审查结果表明，支持向量机 (SVM) 分类器的性能优于 80% 的准确度指标，并被公认为听觉研究领域中最适合的模型。本文讨论了所提出的计算算法的综合迭代特性以及听力受损康复的可行未来方向。