EEG-based Global Brain Activity Analysis of Parkinson’s Disease

The diagnosis of Parkinson’s disease is mainly clinical, and the accuracy of early diagnosis is very low. Electroencephalogram (EEG) has been widely used in the diagnosis and monitoring of various neurological diseases. When correctly interpreted, the diagnostic accuracy is very high. When EEG diagnoses Parkinson’s disease (PD), it is challenging to find accurate biomarkers, which is why EEG diagnosis of PD has not yet become popular. By studying the EEG data, it was found that there are characteristics in the power spectral density (PSD) image calculated using the Pwelch method to analyze and calculate the EEG. According to the characteristics of the image, it can be divided into four categories with different colors: first, it is found that the trend of the red and blue image curves is basically the same, and the energy of blue on the Delta (0.1-4Hz) wave is higher than that of red; the trend of the pink and green image curves is basically the same, and the energy of pink on the Delta (0.1-4Hz) wave is higher than that of green. Secondly, the energy of the red PSD image is very low in the full frequency band, while the energy of the full frequency band in the green, blue and pink PSD images is much higher than that in the red PSD images. It can be inferred that the brain activity of PD patients in the frontal lobe is slower. Finally, it was observed that there were eight symmetrical pairs of EEG sampling points, of which four symmetrical pairs showed obvious fitting inconsistencies, which showed that the PSD of PD patients had asymmetric characteristics. In addition, it was observed that the brain areas with active brain activity, that is, high PSD energy, had very obvious asymmetric characteristics, while the brain areas with lower energy were basically symmetrical.