## 3 Presentation of Components, Methodology of Study and System  
The article presents a comprehensive approach to the problem of classifying an EEG signal significantly disrupted by four selected simulated neural t  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Related Works** & **Keywords** \\ \hline
[36; 37] & LORETA, nervous tics, EEG, biomedical signals, eliminate biological artifacts \\ \hline
[38; 39] & Electro-coulographic artifacts, nervous tics, EEG, biomedical signals, power \\  & spectrum analysis, BCI, insomnia disorder \\ \hline
[40] & Artifact removal algorithm, nervous tics, EEG, biomedical signals, BCI hybrid \\  & system \\ \hline
[41] & Motor imaging, BCI system, IC MARC classifier, EEG, muscle artifacts \\ \hline
[42] & Automatic removal of artifacts, OD-ICA, EEG, ocular artifact \\ \hline
[43] & BCI system, peak detection, EEG, peak frequency detection \\ \hline
[44] & Denoising autoencoder, Lomb–Scargle periodogram, EEG \\ \hline
[45; 46; 47] & Convolutional neural network, skin cancer classification system, deep neural \\  & network, classification of breast cancer histopathological images \\ \hline
[48; 49; 50] & Artificial intelligence in the regulation of automation systems, classification \\  & system \\ \hline
[51; 52; 53] & Artificial intelligence, EEG, spectrogram, convolutional neural networks, classification system \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related works.  
dition, a slight interference was introduced from ambient noise (TV, radio and street sounds) as well as radio waves and Wi-Fi wireless signals. The whole experiment was designed to reflect real conditions as closely as possible. The raw EEG signal was recorded with an Emotiv EPOC Flex Gel headset. The necessary measurements were collected using dedicated EmotivPRO software. The EEG signal subjected to an appropriate multistage filtering was assessed in a two-stage analysis by an expert system. The system consisted of two neural networks implemented in Matlab software. In this case, version 2020b was used. The first of them evaluated the signal in terms of the detection of facial expressions, with the help of which the BCI system was to issue control commands. The classification took place through the consistent preparation of the signal in the classification process, starting with an appropriate filtering and ending with a spectral analysis. The second neural network learned to analyze the signal in search of neural tics. Detailed information on how the expert system works can be found in Section 3.2.  
The electrodes in the Emotiv EPOC Flex Gel headset are presented according to the following channels: Cz, Fz, Fp1, F7, F3, FC1, C3, FC5, FT9, T7, TP9, CP5, CP1, P3, P7, O1, Pz, Oz, O2, P8, P4, CP2, CP6, TP10, FC6, C4, FC2, F4, F8 and Fp2. Table 2 summarizes the set of all channels with their associated names, as shown in Figure 1. The green channels represent the electrodes that were used for the test. The white marks, however, were not used in the study. Moreover, this drawing contains an example of a graphic presenting one of the participants of the study placed in a wheelchair.  
The next section contains the purpose and planned course of the research, as well as the test assumptions.