Parkinson’s disease (PD) is the second most hazardous neurological disease, it deteriorates the people lifestyle. Diagnosis of Parkinson's disease is a complex task due to the inaccuracy of clinical evaluation measurements. Therefore, efficient schemes are needed to act automated evaluation for early detection of Parkinson Disease and to increase the life span. Gait-based medical detection provides positive indications for the presence of Parkinson Disease. Recently, computer vision-based analysis has more demand and effectual in Parkinson Disease investigation. Gait Disorders in PD with the help of EEG Signal can be divided into three steps: first data acquisition next image pre-processing and finally the preprocessed images are given to deep learning methods for classify and detect the Parkinson’s disease. Here, detailed statistical analysis is provided in this review which was conducted by extracting information from 50 papers published between the years 2018 to 2021. Finally, this survey is helpful for researchers in the field of Gait Disorders in Parkinson’s disease Using EEG signal.

Computer vision, Classification, Deep learning, Gait disorders, Preprocessing, Parkinson’s disease.