## 4 Discussion
### Global Discussion  
A different set of papers focused on classifying PD patients or evaluating cognitive impairment. The articles related to the detection of walking alterations presented deficiencies in terms of the patient demographic data and medication status. Specifically, refs. [29; 30; 31; 36; 38; 39; 44; 48; 49; 52; 57; 67] were the articles that considered classification problems and went through the quality criteria specified above. All of them considered balanced classes. Article [48] may also be discarded, as it did not consider balanced classes, so the results could be affected by the majority class. Among the remaining studies, article [36] was also excluded from the comparison, since its objective was the early diagnosis and the results obtained may differ greatly from the classification of patients in advanced stages of the disease. Given that the rest of the articles had accuracy as a common metric, this metric was evaluated for the problem of diagnosing PD by means of EEG through ML techniques, obtaining a value of 97.35 \(\pm\) 3.46%. Consequently, this value could be considered as a baseline for future studies that focus on this diagnostic problem. The sensitivity metric appeared in eight of the articles with a mean value of 96.36 \(\pm\) 5.00%. These studies were performed with different EEG channels, predominating the setups with 14 and 32 electrodes. The low variation in the value provided by the metrics may lead us to think that the number of channels did not influence the classification results. Regarding the models used in recent years (from 2019 to 2021), and taking into account all the results in Table 3, it can be observed that neural models are increasingly used in the classification problem, appearing in 11 of the 16 studies within these years. However, there was no standard in the cleaning process. In 2019, there was an equitable distribution between the three types of processing. In 2020, three of the five articles considered did not perform EEG pre-processing, whereas in 2021, of the nine studies, five considered the removal of artifacts. This analysis indicates, again, that the cleaning protocol did not influence the results of the study. The use of increasingly complex techniques to solve the problem is striking, highlighting the use of CNNs and hybrid models based on CNN + RNN. The articles [32; 34; 36; 45; 47; 53] classified the level of cognitive impairment of PD patients. It should be noted that all of them have been published in the years 2019 and 2021, indicating the novelty of the subject and marking the evolution in the study and diagnosis of PD. In all of them, the elimination of artifacts in the EEG signals was carried out, and the use of the SVM and RF models stands out. Given the novelty of the research topic, it was expected that the models initially used were non-neuronal and that the pre-processing sought to minimize the noisy component from the EEG signals. For this case, we did not consider that there were enough studies that met the proposed quality criteria (description of the medication and patient status, use of balanced classes, and specification of the proposed validation method) to obtain a baseline for this case. However, the mean provided by those studies that passed the cut-off [45; 47] was 77% accuracy. Although [47] did not use fully balanced classes, it did take these differences into account in evaluating the results and performed a five-sample multi-class task, whereas [45] used balanced classes in a binary problem. In light of the previous analysis, growth in the complexity of the neural models, and in the number of studies that address the problem of identifying the cognitive status in patients with PD, is expected. This will benefit the development of a marker of the disease in the early stages. Regarding the methodology, it would be desirable that the studies provide more specific information about the state of the patients and their demographics.