Objective: Since the neurophysiological system is a highly nonlinear dynamic system, nonlinear dynamic information of EMG signals were extracted to describe its characteristics.
Method: Two-channel surface EMG signals were extracted and analyzed to reflect the complexity degree of the dynamics of the neurophysiological system.
Result: Complexity measures of four kinds of forearm motions were calculated and compared. They showed a good separability.
Conclusion: Experimental results proved that this measure, having a simple algorithm, is suitable for short data sets and suitable for real time processing. It provides a new measurable index for both physiological and pathological analysis.