###### Abstract  
Background: Diagnosis of [PERSON]'s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzzyl-guanidine cardiac scinitscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62-99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field. Parkinson's disease (PD); electroencephalography (EEG); machine learning (ML) +
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resolution. It has been widely used to study epileptic disorders and is a widely accessible and low-cost technique. Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but the information processing techniques allow for the extraction of different characteristics that can be of help when characterizing neurological diseases. Due to its good temporal resolution, EEG data provide dynamic information on the electrical brain activity and connectivity. For this reason, EEG has been recorded in different physiological conditions such as basal awake condition, during sleep, during specific sensitive input or cognitive tasks and finally in resting state. During each of these conditions, different networks are expected to be activated and spontaneous connectivity is expected in resting state. In addition to the well known inter/intra-subjects' variability, two main characteristics define the EEG signals and make their subsequent analysis difficult. These are the low signal-to-noise ratio and the stochastic nature of the signals. A low signal-to-noise ratio indicates a high level of noise in EEG signals, so pre-processing is required to filter out the signal noise and to remove the possible artifacts, such as contamination by other biological and non-biological signal sources, and then analyze the signals and obtain results. The main problem to filter the signals is that there are several validated protocols with no gold standard defined to perform the cleaning. Another problem comes from the fact that when the signal noise is removed, relevant components in EEG signals can also be eliminated, which may lead to false diagnosis. On the other hand, the stochastic character indicates that the state or occurrence of an event does not depend on the previous event. Hence, to extract the essential characteristics of the signals, advanced non-linear techniques must be used, which require more expensive computational methods [6; 7; 8]. These difficulties can be overcome by implementing machine learning (ML) techniques to analyze the EEG signals, since they are quite strong techniques that allow to carry out studies of non-linear nature, as well as to deal with raw EEGs.