## 7 Discussion of Obtained Results  
By analyzing the results obtained in Section 6, interesting conclusions can be drawn. First of all, from Figures 17-22 it can be seen that the signals were composed of many very different samples, which included a neutral state, as well as facial expressions responsible for generating forward, rightward and left movements. The signals collected from all participants lasted about 60-70 s. Each of the signals was burdened with eight nervous tics. The authors carefully checked each type of tics and its impact on the quality of classification by the expert system. It should be emphasized that the EEG signal from the four electrodes, which were subjected to a spectral analysis, oscillated around the amplitude of 20 uV. There was significant activity for all four channels for the forward command, while the main activity for the right command was for the Fp2 and F8 electrodes. In turn, the command to move  
Figure 21: An example of the classification of movement commands and nervous tics—neck muscles tightening. Figure 22: An example of the classification of movement commands and nervous tics—neck muscles tightening. left generated a significant increase in amplitude on the F7 and Fp1 channels. The idle state between the forward and right command, and between the right and left command, had a very low amplitude on each of the analyzed channels. These results were in line with expectations. It was possible to achieve an acceptable efficiency for the algorithm, and above all, confirm its applicability to the conditions of everyday life, which was the main goal. It was also important to have a practical approach to the problem that can really affect the lives of people with disabilities. In addition, a system was designed to enable the analysis of filtered signals subjected to the spectral analysis. Most importantly, these signals were heavily distorted by artifacts, yet an expert classification system coped with the problem. It showed a high efficiency in the classification of the EEG signal burdened with many artifacts resulting from various nervous tics. This is an important conclusion because it may indicate the usefulness of the proposed algorithm in everyday life for disabled people with nervous tics. The designed neural network was able to analyze the signal quite efficiently and effectively, disregarding the disturbances, thanks to the splitting of the signal into one-second samples subjected to the subsequent analysis. The applied refiltering of the signal had a huge impact on the quality of the classification of nervous tics. The filtered EEG waveforms seen in Figures 17-22 in section \"b\" were clearly characterized by high jumps in amplitude at the moment of a tic artifact. One can get the impression that it was separated from the rest of the signal. At the same time, it should be noted that the amplitude was lower than the values after the first filtering of the signals (part \"a\" in the figures). The disturbance classification results indicated a one-second or two-second duration of a nervous tic. This was a consequence of the very varied, complex and intricate nature of such artifacts. Summing up the conclusions drawn, it should be noted that apart from measurable disturbances, the subjects were also affected by additional difficulties affecting the measurement of the EEG signal. These disturbances were sound stimuli coming from a loudspeaker, TV or radio, as well as a Wi-Fi network and radio waves. The whole setup, according to the authors, fully reflected the conditions of everyday life. When analyzing the obtained results and the literature proposed in Section 2, it should be emphasized that it constitutes a valuable contribution to the development of several fields. First of all, it contributes to the development of BCI systems, but also to the use of artificial intelligence in classification tasks. The spectral analysis is also an additional research problem. Therefore, it can be said that this article combines the subjects of the proposed literature.