Automatic event detection in surface EMG of rhythmically activated muscles

Precise detection of discrete events in the surface electromyogram (EMG) like the phasic change in the activity pattern associated with the initiation of a rapid motor response is an important issue in the analysis of the human motor system. However, accurate detection is difficult when the muscle is already involved in a secondary motor task, and two superimposed activation patterns have to be separated. This paper describes a method which allows automatic detection of phasic events that arise during simultaneous execution of rhythmical and discrete motor tasks by the same muscle. Based on a nonlinear signal model, events are identified as characteristic changes in the variance of the original EMG signal by using a two-window scheme and the generalized likelihood ratio test. Both, the beginning as well as the end of epochs with prominent EMG activity related to the rhythmical movement and the onset of phasic activity indicating initiation of a discrete contraction can be detected. Problems arising from modelling the EMG by a sequence of independent Gaussian random variables modulated by a deterministic control pattern are discussed