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

TL;DR

This paper introduces a statistical mixture model-based method for accurately detecting movement onset points in brain activity recordings, improving upon traditional variance-based techniques.

Contribution

It proposes a novel MSM-method for movement detection in brain signals, enhancing precision and reliability over existing variance-based approaches.

Findings

The MSM-method effectively detects movement start points.

The new approach outperforms traditional variance-based methods.

Demonstrated advantages in experimental validation.

Abstract

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.