## 6 Results and Discussion
### Evaluation of EEG Signal Cleaning Methods Using SNR Coefficient
Specifically, all processing methods--CNN-LSTM, REG, and ICA--significantly outperformed the raw EEG signals. Furthermore, CNN-LSTM produced significantly higher values than both REG and ICA, suggesting it was the most effective method in this dataset. REG also significantly outperformed ICA, confirming its greater improvement over raw data. In summary, all pairwise comparisons between methods yielded statistically significant differences (\(p<0.001\)), with CNN-LSTM achieving the best overall performance. We obtained very similar SNR values for artifact removal in channels O1 and Oz, confirming that the algorithm performs well for these electrodes and may have practical applications in BCI systems. The SNR\({}_{\rm{SSVEP}}\) alone is not sufficient for evaluating the effectiveness of artifact removal methods. It is possible that, in addition to eliminating interference, a given method may also modify useful EEG signal characteristics--that is, it may remove desired components along with the artifacts. Therefore, in addition to assessing SNR\({}_{\rm{SSVEP}}\) improvement, it is valuable to compare how each method affects signal segments containing artifacts versus artifact-free segments. For this purpose, an analysis of SNR\({}_{\rm{SSVEP}}\) was performed for each method separately, considering one-second EEG signal windows containing artifacts and clean segments. The results are summarized in Table 6. Figure 13 presents the comparison of the average SNR\({}_{\rm{SSVEP}}\) between the raw EEG and the CNN-LSTM for one-second EEG signal windows containing jaw-clenching artifacts and artifact-free segments. It is particularly noteworthy that the CNN-LSTM method applied to signals containing artifacts (average SNR: 2.098) achieves results comparable to those of raw EEG signals without artifacts (average SNR: 2.475; \(p\) = 0.028). Another important observation is that, for artifact-free EEG signals, the SNR values remain comparable--the CNN-LSTM processing method does not introduce significant changes compared to the raw signal (raw EEG: 2.475 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**EEG Signal Without Artifacts**} & \multicolumn{6}{c}{**EEG Signal with Artifacts**} \\ \hline  & Raw EEG & CNN-LSTM & REG & ICA & Raw EEG & CNN-LSTM & REG & ICA \\ \hline mean & 2.475 & 2.653 & 2.725 & 2.701 & 1.173 & 2.098 & 1.847 & 1.462 \\ \hline std & 0.860 & 0.927 & 1.004 & 0.948 & 0.482 & 0.694 & 0.753 & 0.589 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SNR\({}_{\text{RSVEP}}\) for EEG signals with and without artifacts, calculated for one-second time windows containing jaw-clenching artifacts at electrode O2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**Stimulus**} & \multirow{2}{*}{**Raw EEG**} & \multirow{2}{*}{**CNN-LSTM**} & \multirow{2}{*}{**REG**} & \multirow{2}{*}{**ICA**} \\  & & & & & & \\  & & & & & & \\ \hline S01 & 7 & 1.195 & 2.387 & 2.213 & 1.662 \\ \hline S02 & 7 & 0.583 & 1.  
vs. CNN-LSTM: 2.653; \(p\) = 0.34). This indicates that applying the CNN-LSTM method effectively reduces artifact influence to levels typical of interference-free signals while preserving their original structure. In other words, this method accurately reconstructs the actual amplitudes of EEG signals.