Eeg-emg Synergistic Analysis And Correlation Study Based On External-source Load Inspiration

There is a close relation between brain neural activity and muscle activity when human is in action. The EEG–EMG corelation represents the functional connection between brain cortex and muscles. It helps to understand how a brain controls the muscles and also foresees some severe diseases , e.g. Parkinson disease, cortical myoclonus or upper-limb dystonia. At present, the correlation coefficient are commonly used to evaluate the EEG - EMG correlation.But for the traditional definition of the correlation coefficient, the frequency band of EMG is confined to 0-40Hz which is the same frequency band with EEG and more effective information in high frequency EMG are neglected. So it requires to find a more suitable evaluation parameter to charactrize the EEG - EMG correlation。In addition,EEG, EMG and their correlation will change with different external-source load inspiration. Exploration of these changing mechanisms can help better understanding and cognizing the control patterns of the brain on muscle activities.In this thesis, the grip force was taken as an inspiration source. Synchronization acquisition experiments of EEG, and EMG were designed under different inspiration conditions. The changing mechanisms of EEG and EMG with different external-source load inspiration were synergistically analyzed by some methods including power spectral denisity and fractal dimension analysis. Wavelet transform and empirical mode decomposition was combined to extract the EEG and EMG power spectrum in different frequency bands.The results showed that there was no significant relationship between EEG power spectral density and external-source load inspiration while the EMG power spectrum density increased as the inspiration increased. The EEG fractal dimension increased at the beginning and then decreased as the inspiration increased while the fractal dimension of EMG kept a linear relationship with the inspiration. Using the traditional correlation coefficient to evaluate the EEG-EMG correlation, it was found that, as the inspiration increased, the peak area of EEG - EMG spreaded from theαandβbands to theγband.Using the improved correlation coefficien, it was found that the EEG frequency with the highest EEG-EMG correlation coefficient focused in theβband, and the correlation ofγ-band EEG and EMG increased the inspiration increased. Different EEG frequency bands represents different action states. The results of these studies on the changing mechanisms of frequency bands and amplitudes of EEG, EMG and their correlation with different external-source load inspiration will contribute to a better understanding of motor and coordination abilities of human neuromuscular system, and the guidance for the disabled rehabilitation and athletes training in the future.