## 2 Related Works  
In the works of [36; 37], the LORETA technique (low-resolution brain electromagnetic tomography) was used for electrophysiological measurements. It is based on the idea of solving inverse problems and estimating the distribution of the electrical activity of neurons in three-dimensional space. This approach is a linear estimation method that does not supplement the EEG signal with new information but is used successfully to estimate the sources of certain signals in the human brain. The author of the work [36] filtered the signals using software that allowed to eliminate biological artifacts, such as the nervous tics of the test subjects, momentary eyelid clenching and others. The article [38] covers research on the most common type of artifact, belonging to the group of electro-oculographic artifacts--blinking eyes. The research used and analyzed the power spectrum of eye blink artifacts in combination with the use of EEG in the brain-computer interface design. Similarly, in the study [39], the authors performed a spectral analysis of EEG signals in the insomnia disorder. The work [40] described a novel artifact removal algorithm for the BCI hybrid system. To remove artifacts in EEG signals, the authors designed a program that could meet this challenge by means of a stationary wavelet transform in combination with a new adaptive-thresholding mechanism. In turn, the authors in [41] investigated the influence of various artifacts on a BCI system based on motor imaging. Tests were carried out using the IC MARC classifier. The results showed that muscle artifacts adversely affected BCI performance when all 119 EEG channels were used, rather than being configured with 48 centrally located channels. On the other hand, the authors of the article [42] designed an innovative system for the automatic removal of artifacts from the eyes in the EEG. The methods of outlier detection and independent component analysis were used. The test results proved that the OD-ICA (outlier detection and independent component analysis) method effectively removed OA (ocular artifact) from EEG signals, and also successfully preserved significant EEG signals while removing OA. The concept of peak detection with online electroencephalography (EEG) removal for the BCI was also addressed in [43]. The effect of filtering on BCI performance and most importantly on peak frequency detection when using the brain-computer interface was investigated. The filter improved peak detection, but BCI performance was degraded due to movement and further removal of artifacts. The use in [44] of the Lomb-Scargle periodogram to estimate spectral power from partial EEG and a denoising autoencoder (DAE) for training helped to solve the problem of noisy EEG signals. The results showed that the investigated method could successfully decode incomplete EEG recordings with good results. Neural networks and broadly understood classification expert systems including artificial intelligence solutions have long been used in many areas of life and industry. A good understanding of the research topic involving the use of a convolutional neural network as a skin cancer classification system was presented in the paper [45]. Work using a deep neural network in the classification of breast cancer histopathological images was presented in [46], while the article [47] contained a hybrid, convolutional and recurrent deep neural network for classification purposes. Artificial intelligence has also successfully been used in the regulation of automation systems. The works [48; 49; 50] proposed an approach based on an algorithm using an artificial neural network in the task of controlling a magnetic levitation object, music notation classification and a robot. One should also not forget to consider other classification methods based on modern approaches to deep learning such as convolutional neural networks (CNNs). The use of deep CNNs with different two-dimensional representations of the time-frequency sig nal has recently become an interesting research topic.