Synchronization Method for EEG Signals of Body Movements in Patients with Parkinsons Disease * In this paper, a synchronization method of intracranial electroencephalogram (EEG) and body movements based on patients with Parkinsonu0027s disease (PD) is proposed. By using the EEG signals acquisition system provided by a medical company, we develop the external induction electric pulse generator, and realize the synchronization of intracranial EEG (iEEG) and patientu0027s body movements in the video shot. The synchronization method proposed in this paper provides technical support for further analysis of the correlation between EEG and body movements of patients with PD and decoding of EEG, and it also establishes basic conditions for disease diagnosis and treatment by using EEG of body movements. If we can predict the coming symptoms of tremor, rigidity and bradykinesia through the changes of EEG before the onset of PD, even if the time is very short, it is possible for us to intervene and treat PD to a certain extent, which will greatly relieve the damage to patients during the onset of symptoms.

A Deep ConvolutionalRecurrent Neural Network Architecture for Parkinsonxe2x80x99s Disease EEG Classification Parkinsonxe2x80x99s disease (PD), characterized by slowness of movement, tremor and rigidity, is one of the most prevalent neurodegenerative disorders. Recent studies have demonstrated that abnormal neural oscillations within and between multiple brain regions play a critical role in the motor symptoms through invasive neural recordings. Progressions have been also made in EEG studies to use features in cortical oscillations recorded noninvasively as a diagnostic tool for PD. However, it is still challenging to effectively use EEG recordings for PD diagnosis. In this work, we design a novel deep learning framework for PD EEG classification. Specifically, the convolutional neural network (CNN) and the recurrent neural network (RNN) with long shortterm memory (LSTM) cells are exploited in our framework. First, we design two 1DCNN layers to derive features to represent spatial (topological) relationships across EEG channels. Then, we apply LSTM on the spatial features from the CNN to further improve its performance. Finally, we validate our model on the PD classification on resting EEG recorded from 20 PD and 21 healthy subjects. Our method achieves accuracy of 96.9%, precision of 100%, and recall of 93.4% for differentiating PD from healthy controls and outperforms the stateoftheart PD EEG classification results in the deep learning literature.