## 4 Discussion  
PD is a disease mainly characterized by motor dysfunctions which affects the quality of life of patients. The application of ML techniques in EEG may be able to identify diagnostic and progression markers with the potential to be applied in the clinical setting through a simple quick-to-perform test, with a low error rate and at low cost and invasiveness. It can be observed that the oldest article of the nine selected in this review was just three years old, and the number of articles has increased in the following years, showing the novelty and growing development of ML techniques applied in EEG in relation to PD. On the other hand, regarding the global distribution of the selected articles, it can be appreciated that, according to the first affiliation country of the first author, although Asia stands out as the continent with the highest number of publications, the distribution is relatively homogeneous between the continents of Asia, Europe and North America, reflecting a global interest in encompassing the objectives of this review. To assess the quality of the selected articles and facilitate the comparison between them, the content of each article was evaluated using the checklist of the guidelines for developing and reporting machine learning predictive models in biomedical research [28]. This evaluation was carried out by two different evaluators and obtained an average value of \(9.56\pm 1.89\) out of 12, and \(8.89\pm 1.97\) out of 12, respectively, which indicates the good quality of the included articles. The kappa value among the two independent reviewers was calculated, obtaining a value of 0.67, which indicates a substantial agreement between the evaluations. Both evaluators agree that the less fulfilled items were 11 and 12, which are related to the limitations of the model and the unexpected results, respectively. The fact that most of the articles did not include this kind of analysis may be due to the fact that sometimes the scope or limitations were unknown at the time of publication and they only became evident over the years and with the development of new algorithms. Among the different clinical variables studied by the articles, it becomes apparent that a lack of clinical parameters was associated to the state of the PD. As it can be noticed from Table 2, variables like the degree of disease progression according to the HY scale, the state of the disease according to the UPDRS, and the years of duration of the disease, were not provided by all the articles. The lack of information about these variables can influence the classification results and lead to false positives/negatives. For instance, if a binary classification network (Parkinson vs. no Parkinson) is trained with patients in advanced stages of the disease, it may be the case that the model misclassifies a patient with PD in the early stage of the disease. It would be interesting to further evaluate these data, since a classifier may work differently with patients in different phases of the disease. Actually, it should be noted that only [29] did a study classifying the degree of progression of the disease,in which the groups with more advanced stage patients obtained worse results in the classification, nevertheless, these groups were the smaller ones (less than 10 subjects) meanwhile the rest of the stages with better results had more than 20 subjects. ML techniques require a large enough dataset to work properly, so these results suggest that small groups of patients are not sufficient for the model used in that study (SVM). Furthermore, we did not find information in all the articles regarding the medication taken by the subjects, despite the fact that dopaminergic drugs are known to influence the EEG characteristics and therefore vary the classification results. Finally, on the side of the ML models, the information incorporated in the articles is more abundant and homogeneous.