## 3 Results
### Type of EEG Recording
#### 3.4.1 Resting State EEG Group  
The resting state EEG group contains seven articles, which considered different measurement protocols and channels of the EEG recording. Articles [31; 37] were based on the same study but using different features and models, as it can be seen in Table 3. The predominant protocol within this group consisted of recording the EEG in the eyes closed resting state, which was used in four articles. On the other hand, the articles exhibited different recording durations, with an average value of \(6.37\pm 3.10\) min, and a mode of 5 min, which was considered in four of the seven articles of this group. The number of EEG channels also varied inside the resting group, with a low density of electrodes prevailing: 71.43% of these articles considered between 14 and 20 electrodes, whereas in the rest the number of channels exceeded 100 electrodes. Data pre-processing can be divided into two categories, which are EEG pre-processing or EEG cleaning, and data pre-processing or EEG feature extraction. Regarding EEG cleaning, it can be concluded from the summary displayed in Table 4 that there was no standard cleaning protocol, because the EEG was left free of artifacts in three articles, whereas three of the remaining articles only performed a pre-processing with filters to reduce the noise in the signals (artifacts were not eliminated). The protocol was not specified in [36]. On the other hand, regarding the extraction of features, only in [31] the EEG signal was introduced to make a morphological analysis, whereas in the remaining articles, different spectral characteristics were calculated. The results in Table 4 show that both SVM and DFA, considered by articles [35; 36; 37], were the models that provided better classification results (accuracy greater than 90%) for patients with PD vs. controls. Note that [36] recorded the EEG of patients with PD without Levodopa intake. These articles introduced different features of the frequency spectrum into the network and performed different EEG cleaning protocols. On the other hand, [29], which also used SVM, did not classify patients with PD vs. controls, but studied the disease progression. Thus, the precision obtained for that case is not comparable with that of the others. To conclude, in this group, the evaluation of the quality criteria according to the checklist of Table 1 provided an average value of \(10.43\pm 1.05\) out of 12 according to the first evaluator and a value of \(9.71\pm 1.28\) out of 12 according the second one. The kappa value calculated for the items in this group was 0.71. It can be appreciated that this value is higher than the one obtained when considering all the selected articles. This indicates that the evaluators exhibit a greater agreement when restricting to the articles of the resting state EEG group. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Year** & **Accuracy Results** & **Features** & **Artifacts** \\ \hline
[29] & 2019 & 84\%—SVM and 88\%—KNN & Relative power low-electrode density & Free-artifact \\ \hline
[30] & 2017 & 78\%—random forest & Selects the most important characteristics & Free-artifact \\ \hline
[31] & 2018 & 88.25\%—CNN & Not considered & Noise filter \\ \hline
[32] & 2019 & 79\%—CNN and 81\%—RNN & Spectrogram & Noise filter \\ \hline
[33] & 2019 & EEG—62\%, EMG—73\%, and both combined—98.8\% & EEG: non-lineal parameters. & Not specified \\ \hline
[34] & 2019 &
\begin{tabular}{c} ANN 100\% with the \\ quasi-Newton algorithm Trainlm \\ \end{tabular} & EEG: non-lineal parameters. & Not specified \\ \hline
[35] & 2018 & 94.34\%—SVM & Power spectra of bands and regions & Free artifact \\ \hline
[36] & 2020 & 95.24\%—DFA & Coherence in the beta band & Not specified \\ \hline
[37] & 2018 & 99.62\%—SVM & Features of the higher-order spectra & Noise filter \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results, features introduced to the models and signal filtering shown in Tables 2 and 3 for the selected articles.