## 6 Results and Discussion
### Evaluation of EEG Signal Cleaning Methods Using SNR Coefficient  
To evaluate the efficiency of EEG signal cleaning, a measure was proposed that expresses the ratio of the SSVEP signal amplitude at the SSVEP frequency to the average amplitude of the EEG signal frequencies in the 1-100 Hz range. This ratio can be defined as follows:  
\[\text{SNR}_{\text{SSVEP}}=\frac{A_{\text{SSVEP}}}{A_{\text{EEG}}} \tag{1}\]  
where \(\text{A}_{\text{SSVEP}}\) is the amplitude of the signal at the characteristic SSVEP frequency, determined from the spectrum using the Fourier transform as \(|FFT(f_{base})|\), with \(f_{base}\) representing the stimulation frequency. \(\text{A}_{\text{EEG}}\) is the average amplitude of the EEG signal in the 1-100 Hz range, calculated as follows:  
Figure 10: Signal spectra before (raw EEG) and after cleaning with REG and CNN-LSTM methods. \[A_{\text{EEG}}=\frac{1}{N}\sum_{f=1}^{100}|FFT(f)| \tag{2}\]  
Here, \(|FFT(f)|\) is the amplitude of the signal spectrum at frequency \(f\), and \(N\) is the number of frequency lines within the 1-100 Hz range (in this case, \(N=100\)). Figure 11: Spectrogram of the EEG signal from the O2 electrode for user S24 after artifact removal using the CNN-LSTM. Figure 12: Difference in spectrograms between the original EEG signals with jaw clenching artifacts and the signals cleaned with the CNN-LSTM model, shown for electrodes O2 and subject 24 during SSVEP stimulation. A high SNR\({}_{\rm{SSVEP}}\) value implies that the SSVEP signal stands out markedly against the rest of the EEG activity, indicating a strong presence of the SSVEP. Conversely, a low value for this metric suggests the SSVEP is less discernible against the EEG background, indicating limited effectiveness of the particular method. For the original EEG signals, this coefficient is generally low since the SSVEP frequency is overshadowed by muscle-artifact activity. By using SNR\({}_{\rm{SSVEP}}\), it becomes possible to quantitatively compare various EEG processing methods with regard to their ability to extract SSVEP signals, even in the presence of interference or artifacts. Table 5 shows the SNR\({}_{\rm{SSVEP}}\) values calculated for one-second time windows that contain jaw-clenching artifacts, selected during stimulation while jaw clenching. The analysis was performed for different signal processing methods: the original EEG (raw EEG), regression-based artifact removal (REG), the CNN-LSTM-based approach, and independent component analysis, focusing on the O2 channel. Each of these methods aims to improve the quality of the EEG signal, which is reflected in higher SNR\({}_{\rm{SSVEP}}\) values. Higher values indicate more effective extraction of SSVEP and better suppression of artifact influence. The SNR\({}_{\rm{SSVEP}}\) values for the original EEG signals serve as a baseline for comparing the effectiveness of the individual methods. From the presented results, one can observe how different EEG processing approaches handle the reduction of artifact influence and the enhancement of the useful SSVEP signal. These values allow for an assessment of which method most effectively improves the quality of SSVEP detection under challenging interference conditions. Analysis showed that the CNN-LSTM method achieved the highest average SNR\({}_{\rm{SSVEP}}\) (2.098), indicating its effectiveness in enhancing the SSVEP signal against interference. The REG method, with an average SNR\({}_{\rm{SSVEP}}\) value 1.847, ranked second in terms of efficiency. The ICA method (1.462) demonstrated a moderate improvement in the SNR. For the original EEG signal (raw EEG), the average SNR\({}_{\rm{SSVEP}}\) was 1.173, serving as a baseline for assessing the effectiveness of the processing methods. Statistical analysis comparing the effectiveness of different EEG processing methods revealed significant differences between the approaches. Paired-sample \(t\)-tests showed that the differences between methods were statistically significant (\(p<0.001\)), indicating that the observed effects are unlikely to be due to chance.