## 1 Introduction  
Over time, people have been fascinated by the idea of creating an interface that allows machines to \"read\" and interpret thoughts correctly for the automatic execution of tasks. Recent discoveries in the fields of neuroscience and engineering make it possible to translate this idea, seen in the past as a myth, into applications that have proven to be viable, although only in a small field [1, 2, 3]. Although the idea has persisted for a long time, the complexity in its implementation means that, even today when technology has reached huge evolutionary proportions, interfacing the biological with machines (robots/computational structures) [4] is perhaps too much of a challenge. However, through the ambition and perseverance of scientists, researchers and those people eager to find solutions to problemsthat seem impossible to solve, the transition from the prototype stage to the large-scale implementation of brain-computer interface applications is just a matter of time [5]. The implementation of an operative communication solution, coming as an alternative to the use of speech, gesturing hand movements or head movements, is a considerable help in improving the situations of people with severe disabilities [6; 7] whose motor functions are reduced due to accidents or diseases, and have musculoskeletal system difficulties such as parapleggia, paralysis or loss of limbs. BCI systems based on sensorimotor rhythms are known as motor imagery (MI) BCI systems [8]. Sensorimotor rhythms include alpha (8-13 Hz) and beta (14-26 Hz) frequency bands [9; 10]. When a human imagines a motor action without any actual movement, the power of alpha and beta rhythms can decrease or increase in the sensorimotor cortices over the contralateral hemisphere and the ipsilateral hemisphere; this phenomenon is called event-related desynchronization/synchronization (ERD/ERS). EEG allows completely new possibilities to research the activities of the human brain [11]. It uses electrodes placed directly on the scalp to measure the electrical potentials generated by the electrical activity of the brain, which have a small amplitude (5-100 \(\upmu\)V). Due to the skin, bone tissue (skull) and other tissues that separate the electrodes from the actual electrical activity, the signals tend to be attenuated and quite noisy. Therefore, while EEG measurements have a good temporal resolution with delays of tens of milliseconds, the spatial resolution tends to be small, varying, at best, to an accuracy of about 2-3 cm but usually even less. Two centimeters on the cerebral cortex may be the difference between deducing that the user is listening to music when, in fact, he is moving his hands [12]. Therefore, the interface that has as its starting point the potentials generated by the brain (EEG signals) is called the \"brain-computer interface\" (BCI). The first applications for interfacing the biological with machines have their origin in the military field [13; 14; 15]. The competitive environment made these applications develop very quickly, and the ideas were later taken over and implemented in other fields, such as medicine and the video game industry [16; 17]. BCI-based technology is a powerful tool for communication between systems and users. A particularly important aspect is that in BCI applications, there is no need for the intervention of the muscular system. Therefore, one of the main aspects of the studies is to help people with disabilities by implementing solutions that will improve their way of life [18; 19]. A brain-computer interface is a system that allows a person to send commands to an electronic device just by simple voluntary variation in brain activity (thoughts) [20; 21; 22; 23; 24]. These terms refer to an interface that takes signals generated by the brain and transmits them in an interpretable form to electronic devices.