Nonlinear methods in EMG analysis
The methods used in biosignal analysis are most commonly linear in nature. Advantages of linear methods are that they are often quite simple to implement and the interpretation of the results is more or less straightforward. However, in many cases the physiological systems underlying the biosignal are highly complex and, thus, it is reasonable to assume that the mechanism generating the biosignal is nonlinear in nature. The nonlinear characteristics of the biosignal can be analyzed with different nonlinear methods. Properties describing nonlinear signals include concepts such as complexity, dimensionality, stability, and self similarity. These properties can be estimated by calculating nonlinear measures such as entropies, correlation and fractal dimensions, and self-correlation.
The popularity of nonlinear methods has increased substantially during the past decade and they have been applied for various biosignals. Probably as one of the most recent applications nonlinear methods have been introduced to EMG analysis. Currently, our research group has focused on the applications of nonlinear methods on HRV and EMG analysis. One popular nonlinear method which has been used both in EMG and HRV analysis is the so-called recurrence quantification analysis (RQA) which was introduced by J.-P. Eckmann in 1987.
The RQA method can be used to describe the degree of recurrence and determinism in a dynamical system. The RQA technique is based on the detection of state changes. In EMG analysis the RQA analysis has been used to describe the fatigueness of the muscle and the changes in motor unit synchronisation. An example of a recurrence plot for an EMG signal and the signal itself is represented in the figure below. The black dots in the plot indicate recurrent points and the lines parallel to diagonal line indicate system determinism.