###### Abstract
Tests have shown that classification performance and prediction time are similar between the LSTM-RNN model and traditional machine learning (ML) algorithms [15]. A novel multimodal fusion architecture is proposed in [16]. The goal is to calculate the respiratory and exercise parameters and simultaneously identify human actions. The research aims to understand breathing patterns during daily activities. It is possible thanks to the development of a wearable respiratory and activity monitoring (WRAM) system. A hybrid hierarchical classification (HHC) algorithm combining deep learning and threshold-based methods is designed. Emotiv EPOC Flex Gel is a relatively cheap, light, wireless BCI headset which ensures control and effective measurement systems [17]. In the case of these investigations, an extended version of the headset with 32 electrodes, i.e., Emotiv EPOC Flex Gel, was used. Each sensor captures four different brain waves (delta, theta, alpha, and beta) in real time. The investigations using this device (version Flex Gel) are still very rare in spite of the fact that it is one of the most accurate and the most complex devices of the manufacturer [18]. Emotiv EPOC Flex Gel was used, among others, in the works pertaining to the comparison of the influence of smoothing filters on the quality of data registered with the Emotiv EPOCFlex Brain-Computer Interface headset during sound stimulation [19]. It was also used in the work investigating the changes in the activity brain waves during audio stimulation using non-invasive brain-computer interfaces [20; 21]. Other investigations using the set discussed in the paper were presented in the paper on the training system based on brain-computer technology in the field of motor imaging [22] and the publication on recognition of eyeball movements based on EEG [23; 24; 25; 26]. The use of the waves marked with Greek letters: Alpha (8-10 Hz), Beta (13-30 Hz), Delta (<4 Hz), Gamma (>30 Hz), Theta (4-7 Hz) and Mu (8-12 Hz) is currently applied for classification of EEG frequency range. It should be noted that the last ones strictly depend on brain activity and performance. They are also slightly similar to Alpha waves [19; 27; 28; 29; 30]. LEGO Mindstorms EV3 set is an extensive environment for learning mobile robot control. It offers many possibilities for each advancement level in operation of this type of devices. Its popularity made that this system, based on the Linux operating system, can communicate with many popular programs such as, for example, Matlab/Simulink [31]. The investigations carried out with LEGO set cover various aspects. However, there are few investigations pertaining to full control of the vehicle built based on EV3 cube (move forward and backward, to the right and to the left), although there are many investigations referring to the application of EEG signals and BCI technology in physiotherapy, rehabilitation and physiotherapeutic robotics [32; 33; 34]. The aim of this research was to design a system for classifying the EEG signal in the control of a mobile robot. When analyzing the solutions available in the literature, an original approach was chosen. A method with 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 and a proprietary expert algorithm. The resulting data allowed to control the constructed robot. The conducted research can find many applications, because their use is possible in the design of rehabilitation apparatus for disabled people or supporting the movement of wheelchairs.