## 1 Introduction
The reason for this is that less force is needed to detect movements, unlike in the case of conventional interfaces (e.g., joysticks). In [28], authors proposed a Recurrent Neural Network (RNN) for performing trajectory control of redundant robot manipulators, using Remote Center of Motion (RCM) constraints. The proposed solution was based on artificial intelligence algorithms. They can be used, for example, in surgical operations. Demonstration experiments, the purpose of which were to verify the correct functioning of the project, were carried out in a laboratory environment using the KUKA LWR4+ robot. Systems identifying and classifying various conditions, diseases, elements of nature and even those responsible for automatic control of objects have been used successfully for many years. Many of them are increasingly based on artificial intelligence encompassing neural network algorithms. An effective review of research on the use of convolutional neural networks as a skin cancer classification system is presented in [29]. In turn, [30] contains a broad description of the system in which the deep hybrid neural network is used in the task of classifying the histopathological images of breast cancer. In [31], a new hybrid, convolutional and recurrent deep neural network was designed for classification purposes. The deep plexus network was also used in the classification system to diagnose various disease states. Neural networks are also widely used in the regulation of automation systems, and an example can be seen in [32,33], which proposed an artificial intelligence algorithm based on neural nets in the task of controlling a magnetic levitation object, and a robot. These, and many other applications, encourage further research and the expansion of applications of this type of project. As in [5], the Emotiv EPOC Flex Gel set was again chosen. It is a comfortable, wireless BCI headset, which provides many possibilities of use and wide application in numerous projects [34]. It has 32 electrodes, but only some of them were used for the purposes of the study. Research and projects using this kit are very rare, despite its advanced design. The use of Emotiv EPOC Flex took place, among others, in the work on comparing the effect of smoothing filters on the quality of data recorded with the Emotiv EPOC Flex BCI headset during sound stimulation [35]. It was also used in a pilot article that precedes this study and is less advanced [5]. Other kit-based research is included in the article on a BrainComputing Training System for Motor Imagery [36] and a publication on EEG-based eye movement recognition [37; 38; 39; 40]. Currently, the nomenclature presents the following EEG frequency determination, where the Greek letters are used: Alpha (8-12 Hz), Beta (13-30 Hz), Delta (<4 Hz), Gamma (>30 Hz), Theta (4-7 Hz) and Mu (8-12 Hz). The latter are closely related to brain activity and performance, and are also somewhat similar to alpha waves [35; 41; 42; 43; 44]. The aim of the conducted research was to design an EEG signal classification system in controlling a mobile robot with simultaneous verification of pure mental commands using a sensor measuring the EMG signal. This is absolutely crucial, as paralyzed people can control objects only with their thoughts, without the additional support of limb muscle movements. After analyzing the literature, it can be concluded that the proposed approach is original. Building on the previous [5] pilot study and its successful results, a method of filtering the raw signal followed by periodic signal integration was designed and tested. The data obtained in this way was classified by an artificial neural network. In the current approach, it is only part of the entire expert system. This is to make the algorithm more noise-resistant and simplify its handling. In the current approach, the user only controls the robot by imagining the movement of the arm.