## 8 Conclusions and Future Work  
The proposed expert classification system offers many development opportunities. There is also the possibility of further research in terms of its use by disabled people in various types of BCI systems projects. First of all, it would be worth checking how the proposed algorithm performs in the case of people who actually suffer from the nervous tics in question. It would also be worth checking what the intensity limit of this type of tic is for which the algorithm is no longer accurate. Thus, possible weaknesses of the designed system could be found and improved as expected. It would then be known whether the algorithm classifies equally effectively with an increase in the intensity and frequency of the tics, or whether its accuracy decreases. Future research should also consider other types of preclassification steps and the use of other signal modification methods prior to classification. Certainly, further improvements and changes to the system will bring new research results. However, the technical limitations of a given approach should not be forgotten. An example is external disturbance. Most of them are not measurable and should be subject to subjective evaluation. Each of the participants in the study had a different response to sound stimuli and other disturbances. Combining this fact with a significant distortion of measurements by artifacts, it should be noted that some cases may exceed classification capabilities. This is related to the need to train the network and collect new training data, which can be burdensome. Summarizing the results obtained in the experiment, it can be concluded that the expectations set for the system were met. The advantage is the possibility of further research leading to the expansion of the system. Undoubtedly, the greatest hopes are related to the improvement of the project in terms of deep learning. It would be good to test the system on people with disabilities, and above all on people with nervous tic syndromes, the analysis of which was presented in the article. This would certainly make it possible to better adapt it to their needs. The authors trust that then, it could improve their functioning activities in the future. A advantage of the project is its adaptation to real conditions, and it is mainly based on the use of facial expressions as a control element. Mental commands due to so many disturbances would be very difficult to analyze, not only due to the limitations of algorithms, but above all due to the inability for the respondents to sufficiently concentrate. However, it can be concluded that such a system also has disadvantages and limitations. It cannot be used in projects where completely paralyzed people are considered. This is not possible because the analysis of the EEG signals in this case is based on potentials evoked by muscle stimulation, which may not be present in paralyzed people. The authors also added the program code that has a file with the instruction manual. However, the detailed description of the script in this work was omitted for the sake of readability and the size of the article. Conceptualization, D.P. and S.P. ; methodology, D.P. ; software, D.P. ; validation, S.P. and D.P. ; formal analysis, S.P. ; investigation, D.P. ; resources, D.P. and S.P. ; data curation, D.P. ; writing--original draft preparation, D.P. ; writing--review and editing, S.P. ; visualization, D.P. ; supervision, D.P. and S.P. ; project administration, S.P. and D.P. ; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Science Center, grant number: 2021/05/\(X\)/\(ST7\)/34, MINIATURA 5. In the case of this study, it should be noted that it did not require the approval of the Bioethics Committee because it was not a medical experiment. However, there is one point that needs to be clearly highlighted: informed consent was obtained from all subjects involved in the study. The authors declare no conflict of interest.