###### Abstract  
Background: [PERSON]'s disease (PD) affects 7-10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 \(\pm\) 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment. machine learning; deep learning; artificial neural networks; electroencephalogram; Parkinson's disease; review +
Footnote †: journal: _Sci._  
There is a wide variety of techniques in the field of neurology that could be used to support [PERSON]'s clinical diagnosis. These include image-based tests such as SPECT and Cardiac MIBG, both revealing indirect physiological consequences of dopaminergic denervation. EEG tests are non-invasive techniques, low-cost compared to the mentioned image-based tests, and they are present in most health centers. This technique records the electrical activity of pyramidal neurons of the brain cortex and has shown to be effective in other neurological diseases such as in the diagnosis or prediction of epileptic seizures, in the development of biomarkers of [PERSON]'s disease, or even experimentally in the detection of abnormalities in schizophrenia [9; 10; 11]. Moreover, it has a high temporal resolution and a high test-retest, that is, it is capable of reproducing results regardless of external aspects. However, two main characteristics define the EEG signals and make their subsequent analysis difficult: the low signal-to-noise ratio and their stochastic nature. A low signal-to-noise ratio indicates a high level of noise in EEG signals, so pre-processing is required to filter out the signal noise and remove possible artifacts and then analyze the signals and obtain results. The main problem when filtering the signals is that there is no standard protocol to perform the cleaning. Another problem comes from the fact that when the signal noise is removed, relevant components in EEG signals can also be eliminated, which may lead to misdiagnosis. Regarding the stochastic character, it indicates that the state of occurrence of an event does not depend on the previous event. Hence, to extract the essential characteristics of the signals, advanced techniques must be used for the study of nonlinear dynamics, which require more expensive computational methods [12; 13]. Artificial Intelligence (AI) models have shown to be some of the most suitable methods to deal with these difficulties since their increasing development in recent years has made them indispensable tools for analyzing and understanding the large amount of data generated by today's society.