## 6 Results and Discussion
### Visual Evaluation of EEG Signal Cleaning Quality  
In this study, three techniques were used to clean the EEG signals from electrodes O1, O2, Oz, and Cz: regression, independent component analysis, and a CNN-LSTM neural network. The goal was to remove interference caused by EMG muscle artifacts. Figure 9 shows examples of EEG signals at electrode O2 in the time domain before and after each cleaning method for participant S2, illustrating the differences between the original and cleaned signals. Compared to the raw EEG, each method reduced the signal \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **(Real) [\(\mu\)V]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **(Aug) [\(\mu\)V]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Std Dev** \\ **(Real) [\(\mu\)V]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Std Dev** \\ **(Aug) [\(\mu\)V]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Skewness** \\ **(Real) [-]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Skewness** \\ **(Aug) [-]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Kurtosis** \\ **(Real) [-]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Kurtosis** \\ **(Aug) [-]** \\ \end{tabular} } \\ \hline Raw EEG & \multirow{2}{*}{0.022} & \multirow{2}{*}{0.26} & \multirow{2}{*}{9.24} & \multirow{2}{*}{9.65} & \multirow{2}{*}{0.09} & \multirow{2}{*}{0.24} & \multirow{2}{*}{2.4} & \multirow{2}{*}{2.97} \\ (clean) & & & & & & & & \\ \hline EEG & \multirow{2}{*}{\(-0.77\)} & \multirow{2}{*}{1.8} & \multirow{2}{*}{24.17} & \multirow{2}{*}{34.7} & \multirow{2}{*}{0.09} & \multirow{2}{*}{0.16} & \multirow{2}{*}{5.4} & \multirow{2}{*}{5.5} \\ (artefact) & & & & & & & & \\ \hline Fp1 & \(-2.23\) & 1.53 & 97.67 & 60.25 & \(-0.44\) & 0.08 & 11.0 & 10.9 \\ \hline HEOG & \(-0.03\) & 1.19 & 38.6 & 59.51 & \(-0.1\) & \(-0.03\) & 6.1 & 11.02 \\ \hline Nape & \(-0.83\) & 5.33 & 30.31 & 91.02 & 1.21 & 0.33 & 5.7 & 6.92 \\ \hline Cheek & \(-1.2\) & 1.36 & 43.63 & 63.51 & 0.36 & \(-0.02\) & 6.5 & 11.25 \\ \hline Jaw & \(-1.83\) & \(-1.81\) & 72.28 & 61.65 & 0.09 & \(-0.02\) & 3.0 & 10.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of real and augmented EEG signals. Figure 7: Spectrum of O2 electrode signals for user S24 (artifact-free signal with a visible spectral line at 25 Hz and signal-containing muscle artifacts). amplitude during the artifact periods. Notably, the CNN-LSTM method achieved the greatest amplitude reduction during the artifact intervals, while regression (REG) and ICA yielded slightly higher amplitudes. Nonetheless, all methods preserved a cleaned signal that closely resembled the original. Similar results were observed for other participants. Figure 8: Spectrogram of EEG signals recorded during stimulation at 25 \(\mathrm{Hz}\) and jaw clenching at 4, 10, 16, 22, and 27 s, also recorded from participant S24 at electrode O2. Figure 9: EEG signals from the O2 electrode in the time domain before and after cleaning. Figure 10 illustrates the EEG spectra before and after cleaning using the REG and CNN-LSTM methods, showing interference reduction in selected frequency bands. These methods were the only ones that enabled moderate artifact removal while preserving SSVEP. A 25 Hz peak can be seen in the spectrum for both REG and CNN-LSTM. In the ICA method, a clinical neurophysiologist selected the components to remove. In many cases, it was not possible to choose only the components related to noise. As a result, the removed components also contained EEG signals and SSVEP. This made the SSVEP results inconsistent and dependent on the expert's subjective judgment. Figure 11 shows the spectrogram after artifact removal using the CNN-LSTM method for participant S24 at electrode O2. Compared to Figure 8, the artifact-related interference has been eliminated, and the 25 Hz SSVEP is still visible.