  As the second most common neurodegenerative disease, Parkinson's disease has
caused serious problems worldwide. However, the cause and mechanism of PD are
not clear, and no systematic early diagnosis and treatment of PD have been
established. Many patients with PD have not been diagnosed or misdiagnosed. In
this paper, we proposed an EEG-based approach to diagnosing Parkinson's
disease. It mapped the frequency band energy of electroencephalogram(EEG)
signals to 2-dimensional images using the interpolation method and identified
classification using capsule network(CapsNet) and achieved 89.34%
classification accuracy for short-term EEG sections. A comparison of separate
classification accuracy across different EEG bands revealed the highest
accuracy in the gamma bands, suggesting that we need to pay more attention to
the changes in gamma band changes in the early stages of PD.
