## 1 Introduction  
Electroencephalography (EEG) is a technique used to track the electrical activity of the brain by placing electrodes on the scalp. Clinically, EEGs are often used to identify seizures, but the same technology can also be used by scientists to discover different functions of the brain and how those functions relate to human cognition and behavior. The scalp electrodes used during an EEG are only used to record the brain's natural electrical activity. Consequently, there is no greater risk associated with this completely non-invasive brain monitoring technique. Most of the signals recorded are likely to come from neurons that are affected by a lot of bioelectric phenomena, such as action potentials, post-synaptic potentials (PSP) and the phenomenon of long-term depolarization of the neuron. Action potentials are associated with a short-term (10 ms or less) local current flow in the axon and a very limited potential field. This makes them unlikely to underlie electroencephalographic phenomena. PSPs last much longer (50-200 ms) and have a much larger electric field. Thus, they are more likely to become major EEG generators. In a healthy brain, the activation potential of a neuron is conducted along the axon to the end of the nerve, where neurotransmitters are released. However, it is the synaptic potential that is the most important source of the electroencephalogram [1; 2; 3; 4; 5; 6; 7]. EEG is widely used in research involving biomedical engineering, neuroscience and others (e.g., BCI), as well as in sleep analysis and detecting brain abnormalities. The reasons for this are, among others, it is non-invasive and incurs relatively low financial costs. In view of the above facts, the classification of these signals is an important step towards making the use of EEG more practical in use and less dependent on trained professionals [6; 8; 9; 10]. Electromyography (EMG) is a type of electrophysiological test that records the electrical impulses that are generated by muscle activity. In neuromodulation studies, EMG is often used to measure the various effects of stimulation in the motor regions of the brain. In clinical settings, EMG is often used to diagnose nervous and muscular disorders. Thanks to this method, it is possible to assess where the pathologies are located and check their nature. In the clinics, EMG may involve inserting a small needle into the muscles to register electrical activity. In biomedical engineering, researchers more frequently use only surface electrodes, which are placed on the skin to record the electrical activity generated by the muscles under study. Thus, the EMG becomes completely non-invasive and does not pose any health risks [6; 11; 12]. Myoelectric interfaces are also used in rehabilitation technology as an auxiliary device. The EMG signal is one of the biological signals that is commonly used to predict human motor intentions and can be used as part of human-robot collaboration systems. These signals, generated in muscle fibers, have also become the subject of much research because they provide a lot of information for the evaluation of neuromuscular diseases, in particular amyotrophic lateral sclerosis (ALS) [10; 13; 14; 15; 16]. BCI is a technology that allows direct communication between the brain and an appropriate external device. The aim of research on the brain-computer interface is, inter alia, the improvement or repair of human senses or motor activities. EEG signal analysis contributes to the research of BCI technology, which has been successfully covered in various scientific articles [17; 18]. BCI solutions could revolutionize the way people communicate with computers and other devices in the future. They currently have particular application and practical value in systems that use EEG as a means of communication and control for people with physical disabilities.