The Analysis Of EEG-EMG Features For Rehabilitation Exercise

Neurophysiology analysis has been the new hotspot on neurological rehabilitation science field. During the neural control processing of human movement, the control and feedback between brain and muscle play important roles, this connection is reflected by EEG, EMG and the synchronization of EEG-EMG. EEG features and EEG-EMG synchronization could reflect the theory of brain functional control and the functional connection with muscles, and could be benefit for understanding the movement control processing and dyskinesia pathology at the level of system. Furthermore, it could provide the theoretical basis of neural network cooperative work during movement control, and give a new way for functional state evaluation during neurological rehabilitation.Recently several new rehabilitation therapies have been developed combined traditional clinical rehabilitation medicine and theory of neurophysiology, and they receive more and more attention because of the security, efficiency and economy. However, the detail rehabilitation mechanism of these new therapies is still under researching and it’s important to research it from the view of neural group oscillation. Our research is mainly focus on EEG and EMG during motor imagery, movement observation and walking. The aim is to further explore the mechanism of movement control and sensory feedback between brain and muscles, and to provide the neurophysiology evidence for applying to rehabilitation for motor imagery, movement observation and walking.Firstly, this paper introduces the basic structure and function of human brain, the production of EEG and EMG and their characteristics. Then summarizes the research status on EEG and EMG analysis, discuss common pre-processing methods, and compare different EEG feature extraction methods and EEG-EMG synchronized analysis methods.For EEG feature extraction during motor imagery and movement observation, because of the nonstationary dynamic property of EEG, we combined wavelet transform to evaluate the time-frequency spectrum power and quantificate the ERD/ERS in time-frequency domain. The EEG datasets are from 10 healthy subjects who performed the left and right hand motor imagery and movement observation.For EEG-EMG synchronization analysis, 10 healthy subjects’ EEG and EMG were synchronously collected during forward and backward walking. Firstly analyze the EEG-EMG synchronization features in frequency domain under both two conditions; then extract the directional couple features between EEG and EMG though Granger Causality(GC) analysis; at last propose Time-Frequency GC combing wavelet transform to describe the synchronization features of EEG-EMG in time-frequency domain during walking.