In the brain, the max number of the neurons belongs to the birth time. Dissimilar to other cells of the human body, the brain neurons can’t be fixed. So, over the time they die and they can’t be replaced. Generally, Parkinson disease results by the death of the nerve cells. The nerve cells generate the dopamine, which is a chemical material. This substance mainly controls the body motions. So, the quantity of the generated dopamine reduces after dying the nerve cells. Then, this situation begins to affect different communication modes of the brain. This disease appears mostly in people with ages about 50 or higher. Unstable posture, muscles’ stiffness, slow motions, tremor, balance losing as well as the damaged fine motor skill are some initial signs for PD. Statistically, about ten million persons suffer the Parkinson disease (reported by World Health Organization). Once there are no visible motor (or non-motor) signs, it’s hard to detect the PD. Hence, the intelligent detection methods can be useful for early diagnosis of the abnormal signs. These methods are automated diagnosis systems, and they are able to objectively detect the Parkinson disease by EEG signals. Using the EEG signals, functions of cortical (or sub-cortical) segments in the brain can be simply detected. As well, other disease related to the brain such as Alzheimer and epilepsy can be identified by these signals. Thus, the EEG signals are employed in the present work for obtaining a computer-aided system in order to diagnose the Parkinson disease. As reported in the literature, electroencephalogram signals are complicated and non-linear inherently. So, most of the linear feature selection methods can’t precisely apply on EEG signals. Higher complication of these signals results in aggravation of the Parkinson disease, which is because of the non-linear elements of the electroencephalogram signals. Therefore, utilization of the non-linear feature extraction methods will be helpful for separation of the healthy and Parkinson EEG signals. In this way, deep neural networks as a subsection of machine learning methods are efficiently applied on various fields of pattern identification as well as the natural language processing recently. Deep learning methods are a sub-group of machine learning techniques called by deep network structures. This idea was introduced for the first time as cybernetics in. Nevertheless, it didn’t consider as a practical concept because of 3 main limitations including: lack of an adequate dataset, lack of computational tools in case of networks with high dimensions, and lack of effective learning methods.