## 3 Results
### Analysis of the Quality of the Articles  
To evaluate the quality of the publications obtained for the review, the items of the checklist shown in Table 1 were considered to compare the content of the publications. The first evaluator provided an average value of \(9.56\pm 1.89\) out of 12 for the 9 articles, whereas the second evaluator determined an average assessment of \(8.89\pm 1.97\) out of 12. To assess the concordance on the evaluations, the kappa (\(\kappa\)) value was calculated, which takes into account the effect of chance on the observed agreement, obtaining a value of \(\kappa=0.67\). This result indicates a moderate-high level of agreement between the evaluators. To facilitate the analysis on the fulfillment of the checklist items, Figure 2 shows a plot displaying the number of articles that satisfies each of the items. Regarding the content of the articles selected for this review, Tables 2 and 3 below show a summary with their characteristics, from the clinical and computer sciences points of view, respectively, providing a qualitative analysis of the checklist items in Table 1. The aspects that were extracted included: 1. analysis of the quality of the dataset, through the study of the number of patients recruited, the type of EEG recording performed and its parameters. 2. analysis of the pre-processing of the data, through the EEG cleaning protocol used and the features extracted from the EEG, if any. 3. characteristics of the model utilized, specifying if one or more models were used, the parameters of the model architecture and the training and validation methods used. Table 3 includes an additional column with the most relevant results obtained in each of the articles, allowing the analysis of the most representative model parameter pairs for this study. The information summarized in Tables 2 and 3 facilitates the comparison between the different articles and the properties of the studies carried out in each of them. Regarding the objective of the selected articles, eight of them studied classification problems, which seek for the diagnosis of PD by distinguishing between patients with PD and healthy patients or controls. The remaining article classified the degree of cognitive decline of PD. The balance between the number of patients with PD and controls is crucial when using ML techniques, because unbalanced data can lead to errors in prediction. It can be verified that this was a common practice in the reviewed articles, since seven of the eight articles that classified PD considered a balanced dataset. Regarding the number of patients included in the studies, it should be noted that studies with less than 50 patients in each category predominated, with an average value of \(28.20\pm 11.53\) for the group of patients with PD and an average value of \(27.20\pm 7.83\) for the controls. The articles did not indicate whether the number of patients was adequate for the classification problem. Moreover, although the average value of the age of both groups was not specified in all the articles, it was a general practice in all of them to take patients with PD aged between 45 and 70 years, with a mean value oscillating around 60 years, which corresponds to the age of incidence of the disease. On the other hand, the healthy patients, or controls, were chosen so that they exhibited the same demographic  
Figure 2: Plot of the number of selected articles that satisfy each of the items of the checklist introduced in Table 1.  
characteristics as the group of patients with PD. It is worth noting that only four of the selected articles indicated whether the patients had taken their dose of levodopa (three studies performed the EEG in ON state, one in OFF state).