## 1 Introduction
The noninvasive approach based on electroencephalogram (EEG) signals proved to be extremely useful for the development of BCI technology. The EEG method has convincingly proved its high safety and reliability, primarily in the framework of providing a new channel of communication with the outside world to patients if they are immobilized for various reasons [17]. The BCIs neural communication system does not require any pronounced muscular activity from the user, and therefore it can actually function even in patients with severe neurodegenerative diseases of the motor system and disorders caused by spinal cord injuries [18; 19]. The selection of informative features to describe the target patterns of multidimensional EEG patterns often faces a number of problems, including too wide range of parameters and the nature of the analyzed activity in the EEG, in particular, its nonlinearity and nonstationary. Furthermore, it has been experimentally shown that significant individual differences in informative EEG features are observed in different subjects [20]. Although we have previously identified the most significant frequency ranges (such as \(\mu\) (10-13 Hz), \(\beta\) (13-25 Hz), and \(\gamma\) (30-70 Hz)) reliable for the classification of MI-related EEG patterns [3], we assume that the most effective frequency band of the EEG and its severity are determined by the individual characteristics of the subject [20]. It has also been shown that the ability of a person, in principle, to voluntarily induce patterns of brain activity, for example, when mentally performing a movement with a certain limb, is purely individual [21; 22; 23]. Therefore, the BCI user is not always able to operate with the set of control commands offered to them. This problem for the case of a practical need to find and consolidate a new pattern or command is not considered in existing systems. In this regard, it is necessary to develop universal and noise-resistant approaches that allow us to isolate informative signal components to detect EEG patterns of target ideomotor acts. EEG signals recorded in MI tasks, such as legs, left-hand, and right-hand movements, are widely analyzed by various methods for BCI applications. For example, the use of conventional support vector machines (SVM), autoregressive SVM (AR-SVM), CSP-SVM [16; 24; 25], and LDA models [26; 27] provide sufficient performance for neural control. Artificial neural networks (ANN) and algorithms for the classification of EEG-related MI have also been implemented [28]: empirical mode decomposition, radial basis function networks, local approximation, and interpretation of time series [3; 23; 29; 30]. These methods improve the accuracy of recognition and classification of target EEG patterns. On the other hand, data augmentation and deep learning approaches are used to classify MI. Convolutional neural networks (CNN) for feature extraction and MI classification provide better accuracy than conventional methods [31]. CNN and Morlet wavelets have also been applied to EEG to create pattern-similar frames to improve the accuracy of evoked potential analysis [32; 33]. Comparative analysis shows that although the effectiveness of neural network methods is high, they require higher computing power and training time. In this paper, an MI EEG pattern classification approach is proposed (Materials and Methods, D). It consists in (i) detection of the most informative EEG frequency ranges specific to different subjects and classes of MI by linear discriminant analysis (LDA); (ii) highlighting the most informative and shorter time segments of the EEG containing the target MI pattern by LDA transformation of all time segments, which also minimizes noise; and (iii) classification of the transformed data by logistic regression. The paper is organized as follows. In Section 2, a description of the experimental procedures followed in EEG recording and processing is presented, as well as the approach developed. A summary of the results obtained is provided in Section 3.