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One of most popular experimental techniques for investigation of brainactivity is the so-called method of evoked potentials: the subject repeatedlymakes some movements (by his/her finger) whereas brain activity and someauxiliary signals are recorded for further analysis. The key problem is thedetection of points in the myogram which correspond to the beginning of themovements. The more precisely the points are detected, the more successfullythe magnetoencephalogram is processed aiming at the identification of sensorswhich are closest to the activity areas. The paper proposes a statisticalapproach to this problem based on mixtures models which uses a speciallymodified method of moving separation of mixtures of probability distributions(MSM-method) to detect the start points of the finger's movements. Wedemonstrate the correctness of the new procedure and its advantages as comparedwith the method based on the notion of the myogram window variance.

Statistical detection of movement activities in a human brain by separation of mixture distributions