Tinnitus is a prevalent hearing disorder that can be caused by various factors such as age, hearing loss, exposure to loud noises, ear infections or tumors, certain medications, head or neck injuries, and psychological conditions like anxiety and depression. While not every patient requires medical attention, about 20% of sufferers seek clinical intervention. Early diagnosis is crucial for effective treatment. New developments have been made in tinnitus detection to aid in early detection of this illness. Over the past few years, there has been a notable growth in the usage of electroencephalography (EEG) to study variations in oscillatory brain activity related to tinnitus. However, the results obtained from numerous studies vary greatly, leading to conflicting conclusions. Currently, clinicians rely solely on their expertise to identify individuals with tinnitus. Researchers in this field have incorporated various data modalities and machine-learning techniques to aid clinicians in identifying tinnitus characteristics and classifying people with tinnitus. The purpose of writing this article is to review articles that focus on using machine learning (ML) to identify or predict tinnitus patients using EEG signals as input data. We have evaluated 11 articles published between 2016 and 2023 using a systematic literature review (SLR) method. This article arranges perfect summaries of all the research reviewed and compares the significant aspects of each. Additionally, we performed statistical analyses to gain a deeper comprehension of the most recent research in this area. Almost all of the reviewed articles followed a five-step procedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the open affairs and challenges in this method of tinnitus recognition or prediction and suggest future directions for research.

翻译：耳鸣是一种常见的听力障碍，可由多种因素引发，如年龄、听力损失、暴露于高强度噪音、耳部感染或肿瘤、特定药物、头部或颈部损伤，以及焦虑和抑郁等心理状况。虽然并非每位患者都需要医疗干预，但约有20%的受困扰者会寻求临床治疗。早期诊断对有效治疗至关重要。近年来，在耳鸣检测方面取得了新进展，有助于该疾病的早期发现。过去几年中，使用脑电图（EEG）研究与耳鸣相关的振荡性脑活动变化的应用显著增加。然而，大量研究得出的结果差异巨大，导致相互矛盾的结论。目前，临床医生仅凭自身经验识别耳鸣患者。该领域的研究人员整合了多种数据模态和机器学习技术，以帮助临床医生识别耳鸣特征并对耳鸣患者进行分类。本文旨在回顾那些以EEG信号为输入数据、使用机器学习（ML）识别或预测耳鸣患者的研究。我们采用系统性文献综述（SLR）方法评估了2016年至2023年间发表的11篇文章。本文对所有被综述研究提供了完整总结，并比较了每项研究的关键方面。此外，我们进行了统计分析，以更深入地理解该领域的最新研究进展。几乎所有被综述文章都遵循了五步流程来实现耳鸣识别或预测的目标。最后，我们讨论了这种耳鸣识别或预测方法中存在的公开问题和挑战，并为未来研究方向提出了建议。