Multi-channel electromyography coupling analysis method based on variable-scale symbol transfer entropy

Abstract

The invention discloses an analysis method of multi-channel brain electrical coupling based on variable scale symbolic transfer entropy, which firstly collects the multi-channel electroencephalogram (EEG) signals and the surface electromyography (EMG) signals on the relevant muscle groups of different gripping actions at the same time. Then the variable scale parameters are selected to mark the EEG signals of the left and right hand under the same grip, and the entropy of the symbolic sequence is calculated. By synthetically analyzing the average value and standard deviations of the transfer entropy and calculating the used time, a suitable and effective symbolic scaling parameter is selected for further analysis. The left hand / right hand movements, EEG signals and corresponding EMG signals of different gripping actions and multiple channels are further analyzed and compared. Finally, according to the change of transfer entropy of EEG to EMG and EMG to EEG, a representation method of coupling intensity of EEG signals is proposed, which reflects the coupling strength between the cortical muscle and the motor muscle objectively and quantitatively.

Description

technical field [0001] The invention belongs to the field of biological signal processing, and relates to a method for analyzing the functional coupling of cerebral cortex muscles and exercise muscles. Background technique [0002] Electroencephalogram (Electroencephalogram, EEG) is the signal generated by the spontaneous or rhythmic activity of brain nerve groups recorded by electrodes, which reflects the potential changes of nerve cell groups in brain functional areas. Electromyography (EMG) is the signal generated when muscle group activity is recorded through electrodes. In the process of human voluntary action, in addition to the synergy between different brain regions, the synchronization between the relevant brain regions and the peripheral nerves related to muscle tissue also automatically occurs. This two major systems of cerebral cortex and motor neuromuscular tissue The internal and mutual effects constitute the cortical-muscular functional coupling relationship ...

AI Technical Summary

Problems solved by technology

However, the traditional time series symbolization method uses the same symbol to represent positive and negative values ​​with the same absolute value, which is likely to cause confusion about the original signal and lose the dynamic characteristics of the signal; on the other hand, the fixed symbolization parameters will also lose Some features of the signal

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