## 1 Introduction  
Currently, intelligent methods of data processing have begun to play a crucial role in the personalization of various areas of human activity. Interaction with technical systems is one of them, and it is referred to as human-machine interfaces. The availability and miniaturization of computer technology has created the prerequisites for the widespread use of new-generation human-machine interfaces, such as brain-computer interfaces (BCIs) or neural interfaces [1, 2]. The development of BCIs is carried out in a multidisciplinary approach at the intersection of information technology and neuroscience and is, perhaps, one of the most intensively growing and promising areas of applied research. The primary goal, as is widely known, is to create a new reliable communication channel for the rehabilitation of people with speech and motor activity disorders [3, 4]. BCI implements its communication function by decoding various types of mental commands of an individual, including evoked brain activity (ERP) and voluntary EEG-induced patterns (motor imagery and inner speech) that are formed in the brain electrical activity. However, at present, the limiting factor for the practical implementation of such systems is the lack of sufficiently developed intelligent data processing methods that provide automated individual interface adjustment [5, 6, 7]. This leads to the urgent unsolved problem of bringing such systems out of scientific laboratories to the end-user environment. One of the unsolved problems hindering achievement that is the exclusion of the laborious participation of an expert researcher, who forms training samples manually, from the process of tuning the neural control system. Therefore, the development of computational methods that provide autonomous BCI tuning is urgent. In numerous BCIs based on the patterns of the motor imagery (MI) acts electroencephalogram (EEG), at the stage of setting up the system, the stimulus-dependent experimental paradigm is used. It consists in presenting the user with various stimuli that specify the execution of single movements (Berlin BCI [8], Graz BCI [9], Wadsworth BCI [10]). On the one hand, within this paradigm, it is convenient to configure and test computational methods for recognizing control EEG commands and monitoring the user's training for their reproduction. On the other hand, control stimuli affect the EEG of a movement's mental performance, since the MI signal and the components of the evoked potential are superposed [11; 12]. Subsequently, this complicates the operation of the BCIs configured in this way in the control mode, in which the MI activity is performed by the user in an arbitrary manner without reference to external stimuli. Therefore, it is urgent to develop a BCI adjustment procedure that does not use external stimuli that induce the execution of individual ideomotor acts. Methods for detecting EEG patterns of target motor imagery acts are a key element in the implementation of tuning autonomy in completely stimulus-independent BCIs. Voluntary imaginary hand and leg movements are actively used in BCIs within the framework of the stimulus-independent experimental paradigm. It is well known that the main advantage of this experimental approach is that mental actions are not associated with external stimuli on the monitor screen, and therefore the control commands themselves are completely voluntary. Thus, the mental formation of control commands for external devices occurs at a random moment at the request of the subject. This allows the user to work at a voluntary pace that is convenient for them. Existing methods of accounting for individual characteristics of brain activity and BCIs based on mental performance of movements [13; 14; 15; 16] were proposed only for particular tasks, with a fixed set of EEG commands known a priori and are difficult to implement for completely stimulus-independent autonomously tuned BCIs.