## 4 Discussion
However, it stands out that the absence of metrics associated to the area of medicine and the clinical setting, such as sensitivity, specificity, true positives, etc. The lack of both these metrics and information concerning the state of the patient lead us to think about the necessity for new translational studies that incorporate these variables. Regarding the quality of the EEG signals, it is conditioned both by the EEG recording parameters and by the EEG acquisition protocol. Within the recording of the EEG signals, although the number of electrodes and the duration of the EEG test vary among articles, they do not affect the quality but rather the spatial resolution of the EEG signal, which is outside the scope of this review. Nevertheless, although the number of electrodes does not affect the quality of the signal, a high density of electrodes may benefit the study of some neurological diseases with a widespread pattern of involvement in the brain. Furthermore, there are parameters of the EEG recording, such as the sampling frequency, that can affect the result obtained from the variables calculated from the EEG, and therefore can influence the quality of the study. Finally, since PD is characterized by motor dysfunctions, it is striking that the resting state tests predominated over the tests associated with motor activation, such as the finger tapping test or the wrist extension and flexion test, which were only considered only in two of the selected studies. This may be caused by the influence of the abundant publications available of neuroimaging studies on the resting state in neurological diseases as PD. In the articles of this review, two types of data pre-processing were evaluated, which are the cleaning of the EEG and the extraction of EEG characteristics. As can be seen in Table 2, there was no standard cleaning protocol for the EEG. This makes it difficult to perform an evaluation of the dataset, since it is not possible to evaluate the loss of elements in the EEG and how these affect the results of the classification problem. As shown in Table 3, there was also a great heterogeneity in the features that were extracted from the EEG. However, it should be noted that spectral characteristics predominated. This may be due to the fact that the spectral features provide information on variations in the EEG bands, and alterations in these bands provide more clinical information than a morphological analysis of the signal, especially in Parkinson's disease, where visual alterations in the EEG signals of patients with PD are not observed. To evaluate how the extraction of features affects the accuracy of the model, we must take into account the architecture of the ML model used. ML techniques allow the analysis of large amounts of data, as well as the extraction of essential characteristics from them. Hence, the choice of the model is influenced both by the size of the dataset and the nature of the data. In the case of this review, the dataset of the selected articles was composed of EEG, and the subsymbolic models are precisely those designed to estimate relationships among data. For this reason, one may expect the subsymbolic models to be the most used ones. This was confirmed by the summary shown in Table 3 and more specifically, by Figure 3. Furthermore, in both of them, it can be appreciated that the most widely used techniques, within subsymbolic processing, were those classified as ANN, i.e., CNN, MLP, RNN and PNN. However, these techniques require a large amount of data for their training, and since in the medical field it is more difficult to obtain data to constitute the dataset, this may justify that the most used individual model was SVM. It is worth emphasizing that ML techniques are continuously growing, and given the novelty of this field of study, there is still a lack of applications for the most complex and novel techniques (like CNN and RNN), which have only been considered in a small number of studies.