Analysis method of multi-channel brain electrical coupling based on variable scale symbolic 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 functional coupling analysis method of cerebral cortex muscles and exercise muscles. Background technique [0002] Electroencephalogram (EEG) is a signal generated by the spontaneous or rhythmic activity of the brain nerve group recorded by electrodes, and it reflects the potential changes of the nerve cell group in the functional area of ​​the brain. Electromyography (Electromyography, EMG) is a signal generated when the activity of muscle groups is recorded through electrodes. In the process of human autonomous action, in addition to the synergy between different brain areas, the related brain areas and the peripheral nerves related to the muscle tissue are also automatically synchronized. This is the two major systems of the cerebral cortex and the motor neuromuscular tissue. The internal and mutual interactions constitute the Cortical-Muscular Functional Coupling (CMC)...

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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