###### Abstract  
This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot. EEG; Emotiv EPOC Flex; NeuroHeadset; cognitive mental; LEGO Mindstorms; brain-computer interface (BCI); motor imagery (MI), neural network; artificial intelligence +
Footnote †: journal: Applied  
by a user for giving commands to a computer are based on muscle movements [9]. Brain-computer interfaces which use electroencephalography (EEG) have been investigated for many years as a communication and control means for physically disabled persons. Through trainings, people may learn to use imaginary motor movements as computer input data or control assistive technology. Article [10] provides review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). It compares the classification algorithm for a specific BCI in terms of performance. Authors found that the classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, as well as other classifiers. The approach involving the use of a neural network, which was used in this pilot study, was also discussed in these publications [10; 11]. The article [12] illustrates the functions of the OpenViBE platform with two VR applications based on BCI. In the first one, users can move a virtual ball by imagining hand movements. The second one allows control of a virtual spaceship using real or imagined foot movements. The online tests of the application, taking into account the evaluation of the platform computational performances, showed that it can be useful for designing VR applications controlled by BCI. The paper [13] presents an introductory overview of BCI platforms. The main research focus was on the OpenViBE platform. Importantly, the results were contrasted with other well-known BCI platforms such as BCI2000, BCILAB, Biosig and Fieldtrip. The work considers BCI platforms as development and experimentation toolkits for researchers, clinicians and others wish to develop BCI and related aspects. The authors described the BCI processing steps, which consist of: signal acquisition (standardization), processing and classification of the obtained signals, and the final feedback. The recurrent neural network (RNN) scheme to perform the trajectory control of redundant robot manipulators using remote centers of motion (RCM) constraints was proposed in article [14]. This is an artificial intelligence approach that can be used, for example, in surgical operations. Demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+. Their purpose was to verify the correct functioning of the project. The literature review also includes a novel, multi-sensor, guided hand gesture recognition system for a surgical robot teleoperation. In this case, the multi-sensor data fusion model was used. The authors used an approach consisting of a multi-layer Recurrent Neural Network (RNN). The network included a a long-term memory module (LSTM) and a dropout layer (LSTM-RNN) is proposed for the classification of multiple hand gestures. Then, the recognized hand gestures are used to perform a set of human-robot collaboration tasks on a surgical robot platform.