## 5 Conclusions  
Thus, in this study, it was observed that the MI activity of the brain is accompanied by a number of electrographic phenomena that can be used in BCIs. Motor imaginations are used by a number of groups [1, 42] to create prototypes of such systems, and in terms of efficiency (reliability, speed control, etc. ), they are not inferior to systems operating on other phenomena, particularly on the basis of P300-evoked potentials. Their significant advantage is that, on the one hand, they do not require external stimuli and are associated with more local cortical phenomena than other types of cognitive activity, and, on the other hand, they are distinguished by a relatively short duration, which is essential for creating real-time systems. Nevertheless, it is currently possible to form a fairly limited alphabet of MI control commands. This apparently is due both to the lack of skills of effective MI activity in a person, with the exception of mental or inner speech [59], as well as to the complexity of classifying electrographic patterns correlating with visual and proprioceptive types of MI [60, 61]. The development of such classifiers is an urgent task that many groups of researchers are currently working on. It is obvious that such important factors as increasing the reliability of methods for detecting and classifying invariant EEG patterns generated by the user in voluntary and anthropomorphic mode directly affect the development of BCI technology. The successful solution of these problems can determine the overall effectiveness and stability of neural communication systems, which will contribute to the rapid spread of technology among paralyzed individuals. The demand for these technologies is quite high, both in the framework of scientific research and in the mass consumer market to solve the problems of monitoring the functional state of a person, as well as the formation of a new nonmuscular, auxiliary control channel for external devices for various purposes [62, 63]. In this paper, it was experimentally demonstrated that the problem of classifying MI EEG patterns or equivalents of real movements can be solved effectively by searching for specific frequency ranges that are individual for the subject. At the same time, the sequential limitation of the time window of EEG epochs within the framework of the procedure for finding the best time segment containing target MI patterns additionally contributed to an increase in the accuracy of classification. In particular, the best classification accuracy of 71.6 \(\pm\) 3.9% was obtained when (a) the search for discriminative frequency bands was performed using power spectral density features, (b) the search for discriminative time segments was performed on the basis of interchannel correlation coefficient features applied to the signal filtered in the bands found during the previous step (three best frequency bands by absolute values of their weights in the linear combinations), and (c) logistic regression was used to classify the transformed data from the previous step. In addition, as part of the development of successful classification models suitable for practical use in the end-user environment, it is necessary to solve the additional task of significantly improving and developing the experimental paradigm of training subjects in the neural interface circuit. These preliminary results obtained by us will become the basis for the development of an adaptive approach in BCI technology. In particular, new methods of deep and machine learning will facilitate the improvement of the accuracy of the classification of MI execution. The development of such models is the subject of our current work. A.S.: software and validation, writing--draft preparation; I.S. : conceptualization, methodology; D.S. : visualization, investigation; D.L. : data collection, writing--draft preparation; V.K. : reviewing and editing. All authors have read and agreed to the published version of the manuscript. The research was supported by the Russian Science Foundation (project no.