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.

翻译：帕金森病是第二大常见神经退化性疾病,它在全世界造成了严重的问题。然而,PD的原因和机制并不明确,也没有确定PD的系统早期诊断和治疗。许多PD病人没有被诊断或误诊。在本论文中,我们提出了一种基于EEG的诊断帕金森病的方法。它用内插法绘制了电脑图(EEEG)的频带能量信号到二维图像的信号,并用胶囊网络(CapsNet)进行了分类,并实现了短期EEG各节89.34%的分类精确度。对不同EEG波段的分类精确性进行对比表明伽马波段的精确度最高,表明我们需要更多地关注PD早期阶段伽马波段变化的变化。