## 5 Conclusions  
The objective of this review consisted in the study of ML techniques applied to the analysis of EEG associated with PD. The search process carried out on 14 February 2022 yielded 358 results that were submitted to the selection process following the PRISMA guidelines. This process resulted in 59 articles dealing with this topic, from different perspectives or with different objectives. Although they were mainly focused on the diagnosis of PD, studies on the classification of cognitive impairment and prediction of alterations in walking were also found. These studies were analyzed according to three key points in the development of ML techniques, which were the dataset quality, the data preprocessing, and the model used.  
The most widely used models were SVM and ANN, with ANN encompassing both MLPs and those ANNs not specified (but suggested to be MLPs), followed by KNN, RF, and CNN. Although the most used individual model was the SVM, the DL techniques, with CNN models, predominated in the diagnostic problem in recent years. Currently, research has focused on the identification of cognitive impairment in patients with PD, in light of articles from recent years (2019 and 2021). The importance of the validation process should also be highlighted. In particular, the k-fold cross-validation method (with k = 10) was used in most of the articles, which allowed the objective evaluation of the results, eliminating the bias produced by the choice of the subjects and facilitating future applications in the clinical field.  
Among the main results provided by this analysis, it was found that neither the EEG cleaning protocol nor the number of channels were relevant to improve the performance in the classification or diagnosis of PD, although the 14-electrode setup was the most used in these studies. In addition, the content of the studies whose objective was the diagnosis of PD was evaluated. Within this group of articles, those satisfying quality criteria were selected to provide a baseline for the accuracy metric, yielding a value of 97.35 \(\pm\) 3.46%.  
According to the analysis performed, future research directions are recommended to be based on the identification of the cognitive status of PD patients, to aid in the early diagnosis or staging of the disease. Moreover, studies are encouraged to incorporate and take into account patient demographics, carry out a k-fold CV for results validation, and use various metrics to provide a global view of the model's performance.  
Conceptualization, A.M.M., A.J.G.-T. and J.P.R.M.; methodology, A.M.M., A.J.G.-T. and J.P.R.M.; software, A.M.M.; validation, A.M.M., A.J.G.-T. and J.P.R.M.; formal analysis, A.M.M.; investigation, A.M.M., A.J.G.-T. and J.P.R.M.; data curation, A.M.M.; writing--original draft preparation, A.M.M., A.J.G.-T. and J.P.R.M.; writing--review and editing, A.M.M., A.J.G.-T. and J.P.R.M.; visualization, A.M.M., A.J.G.-T. and J.P.R.M.; supervision, A.J.G.-T. and J.P.R.M.; project administration, A.J.G.-T. and J.P.R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON] for her contribution to the screening process.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.