  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.
