## 8 Discussion of Obtained Results  
In order to discuss the obtained results, attention should be paid to the originality of the proposed system in terms of EEG signal classification, along with the verification of mental commands without muscle movements performed using the EMG signal. The project describes the structure and components of an expert system composed of several key elements. Algorithms that include artificial intelligence and the classic approach to classification work together in it. The applied division of tasks is not pointless, as such a solution enables efficient classification and effectiveness of the proposed system. A system based solely on a neural network could have some drawbacks and face many difficulties in distinguishing the three key aspects. They are: forearm movement image recognition, artifact recognition and EMG signal verification in terms of finding arm muscle movements. The main goal of the system was its effectiveness and reliability when tested by different participants. By observing the results shown in Figures 18-27, we can see that the classification of signals is correct. The system recognizes the user's mental commands very well. Correct recognition of artifacts in the signal is also worth noting. Their presence is used as a \"switch\" for further control commands of the mobile robot. Thus, blinking the right eye is characterized by the activity of the FC6 and F4 channels, while blinking the left eye causes an increase in the potential on the FC5 and F3 electrodes. Of course, the movement of both eyelids will make all four channels active. This solution is very effective and unambiguous for the classification system. However, its drawback should be noted. It cannot be used for completely paralyzed people, in whom it is impossible to move the eyelids of the eyes. First of all, we need to consider the results presented in Figures 18-27. They are the most crucial as they show the nature of the functioning of the classification system. The 10 participants who took part in the tests were people of different ages, of different sex, and differed in other factors, which could, to a greater or lesser extent, influence the results. The results obtained in the Table 1 allow us to work out many conclusions. Their essence is crucial in assessing the entire designed system. First, it should be noted how diverse the mean value of the integrated EEG signal is for 10 people. It is important to note that the raw characterization would be even more difficult to classify. Each of the study participants made it possible to obtain completely different results obtained in the measurements. The authors emphasize that this is the average measurement of the integrated signal from all 15 trials for each participant and should not be directly related to the results obtained in Figures 6-15. From Table 1, it can be clearly seen that the course of the measured value for each person was completely different and its average range oscillated up to: 886 \(\upmu\)V for the electrodes \(FT8/FT7\), 785 \(\upmu\)V for \(FC6/FC5\) electrodes, 795 \(\upmu\)V for \(F4/F3\) electrodes, and 856 \(\upmu\)V for \(F8/F7\) electrodes. As part of the research, it was decided to use pairwise recording of channels (e.g., \(FT8/FT7\)),  
Figure 29: An example of interrupting the control of the robot when the arm muscles are moved. because depending on the participant, his brain activity during visualization of movement was variable depending on the activity of a given part of the cerebral cortex. The second point that should be noted is the wide scope of operation and classification of the integrated signals by the designed expert system. The used neural network should correctly classify the motion selection using different signals with varying amplitudes. Care was taken to ensure that its functioning allowed for the correct interpretation of the EEG waveforms. This state of affairs shows that the algorithm is resistant to signal variability, which depends on the measurements obtained on the participants of the study.