  One of most popular experimental techniques for investigation of brain
activity is the so-called method of evoked potentials: the subject repeatedly
makes some movements (by his/her finger) whereas brain activity and some
auxiliary signals are recorded for further analysis. The key problem is the
detection of points in the myogram which correspond to the beginning of the
movements. The more precisely the points are detected, the more successfully
the magnetoencephalogram is processed aiming at the identification of sensors
which are closest to the activity areas. The paper proposes a statistical
approach to this problem based on mixtures models which uses a specially
modified method of moving separation of mixtures of probability distributions
(MSM-method) to detect the start points of the finger's movements. We
demonstrate the correctness of the new procedure and its advantages as compared
with the method based on the notion of the myogram window variance.
