## 3 Results
### Analysis of the Quality of the Articles
Regarding the degree of the progression of the disease, a general lack of data can be noticed according to the information summarized in Table 2. Only six articles specified the status of the patients according to the Hoehn-Yahr (HY) scale, four of them considered HY: 1-3, and two of them only considered patients in early stages of the disease (HY 1 and 1.5). None of them included patients in the most advanced phases of the disease, which may be a limitation to evaluate the ability of the results to be extrapolated or evaluate the disease progression. Similarly, only three articles showed the state of the patients according to the UPDRS, with an average value of \(34.43\pm 6.43\). The duration of the disease was specified in four articles, with an average value of \(6.38\pm 1.35\) years. With respect to the parameters of the EEG recording, one may notice that the number of EEG channels varied among the different studies. An EEG recording with a high density of electrodes (greater than 100) was used in two articles, whereas a low density of electrodes (fewer or equal than 20) was considered in five articles with an average value of \(16.2\pm 2.72\) electrodes. The remaining two articles used EEG recordings with only two channels, which they considered a technique that combined both EEG and EMG. It should be remarked that these articles were related to the same study, carried out by the same research group. The EEG recording time was also variable between the articles, showing heterogeneous values again. The test mostly performed with a duration of 5 min, which appeared in four articles. Regarding the pre-processing, it is possible to distinguish between the EEG cleaning protocol (shown in Table 2), and the feature extraction from the dataset (shown in Table 3). The EEG pre-processing, or EEG cleaning, varied from one article to another, mainly due to the lack of a standard EEG cleaning protocol. This makes it difficult to assess the quality of the dataset. In particular, three of the articles performed the EEG pre-processing by removing signal artifacts, three articles minimized the signal noise through the filters, and the remaining three articles did not specify the cleaning process, which leads us to think that no alteration in the EEG signals was carried out. On the other hand, associated with the dataset pre-processing for the input of the model, it should be remarked that the features extracted from EEG signals were very different in between the articles. However, all of them were extracted from the frequency spectrum, and there was only one article in which no data pre-processing was performed. Regarding the ML models, as shown in Table 3, the nine selected articles made use of a total of 11 different ML techniques in order to carry out the classification problems to distinguish patients with PD and controls. The number of techniques exceeds the number of articles due to the fact that, whereas in five articles, a unique model was considered, in the remaining four articles, different techniques were compared. Concerning the type of processing, it is worth noting that the models associated with a subsymbolic processing predominated over those related with a symbolic one. To conclude this analysis of the information summarized in the Tables 2 and 3, it should be pointed out the great heterogeneity between the articles from the point of view of the model used and the absence of a baseline that allows the comparison between the different studies, making it especially difficult to discuss the information displayed in the model parameters and validation columns. The results obtained in each article will be discussed in the subsequent sections of this review. \begin{table}