The aim of the study is to build human-machine control systems. The main methods for con-structing such systems, methods for isolating evoked potentials in electroencephalograms. The article presents studies of electroencephalogram signals with steady state visual evoked potentials for different frequencies of photostimulation, based on the method of multivariate synchronization index. The influence of the length of the processed window on the accuracy of recognition of the frequency of the studied signal is considered. In the course of research, the authors verify the need for pre-processing of the original signals by means of bandpass signal filtering. In addition, the possibility of using a multi-dimensional synchronization index in multi-channel mode is being considered. The result of the authors study is recommendations on the parameters used to high-light the established visual evoked potentials in the method of multivariate synchronization index. The possibility of using algorithms based on a multivariate synchronization index in real time is shown. The results obtained are of practical importance, since they can be used to build neurocomputer interfaces based on visual evoked potentials and can be further used in the for-mation of control theory of robotic systems for various purposes and in the implementation of solutions for the organization of human-machine interaction in narrow practical problems.
The paper deals with the problems of increasing the depth and increasing the long-term stability of communication channels in human-machine interfaces, built on the basis of data on the electrical activity of the forearm muscles. A possible solution is to use the method of analysis of electromyogram (EMG) signals, which combines vector and command control. In view of the possibility of random displacement of the position of the electrodes during operation, a mathematical model was built for vector analysis of EMG in spherical coordinates, which is invariant to the spatial arrangement of the electrodes on the forearm. Command control is based on gesture recognition by means of a pretrained artificial neural network (ANN). Vector control consists in solving the problem of calibrating the channels of EMG sensors according to the spatial arrangement of the electrodes and calculating the resulting vector of muscle forces used as an additional information channel to set the direction of movement of the operating point of the control object. The proposed method has been tested on actually recorded EMG signals. The influence of the duration of the processed signal fragments on the process of extracting information about the rotational movement of the hand was investigated. Since the change in the position of the electrodes between operating sessions is different, an algorithm for reassigning and calibrating the amplification of the EMG channels is presented, which makes it possible to use a once trained ANN for recognition and classification of gestures in the future. Practical application of the results of the work is possible in the development of algorithms for calibration, gesture recognition and control of technical objects based on electromyographic human-machine interfaces.
