## 5 Discussion  
The results obtained can be converted into the information transfer rate (ITR), which are commonly used to compare brain-computer interface (BCI) systems. Table 4 compares the ITR results for individual users using both the CCA and CNN methods. The calculations reveal significant variations in the practical usability of the BCI interface among different individuals. It is important to note that the ITR was calculated based on the classification of one second continuous EEG signal segments. The decision-making time and classification accuracy substantially influence the information transfer rate. In practice, the actual ITR would be lower than the estimated values. Nonetheless, we can approximate the disparity in ITR by comparing the CNN and CCA methods for EEG signal classification. The largest difference, favoring the CNN method, is observed for user S02, at approximately 60.3 bits per minute, and the smallest for user S03, at 1.3 bits per minute. It is important to consider that the analyses were conducted on SSVEP signals recorded under specific conditions and with individuals who had no previous experience with SSVEP interfaces, utilizing only three EEG signal electrodes. Various types of amplifiers, stimulation methods (such as stimulus brightness and LED size), and numbers of stimuli have been employed for recording SSVEP signals in the literature, complicating the comparison of classification results and ITR values across studies. In publication [38], EEG signals recorded using 8 channels and 12 stimulations were utilized, and the FB-EEGNet algorithm applied for classification yielded an ITR of 70.45 bits/min. In publication [49], a method based on task-related component analysis (TRCA) and an extended method based on canonical correlation analysis (CCA) for a 40-class SSVEP were implemented, with the online BCI speller achieving an average ITR of 325.33 \(\pm\) 38.17 bits/min. Lastly, in \begin{table}
\begin{tabular}{l l l} \hline \hline
**Subject** & **CNN** & **CCA** \\ \hline S01 & 59.8 & 47.5 \\ \hline S02 & 76.8 & 16.5 \\ \hline S03 & 5.95 & 4.6 \\ \hline S04 & 47.5 & 16.5 \\ \hline S05 & 47.5 & 27.7 \\ \hline Mean & 42.0 & 19.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ITR comparison for classifiers [bit/min]. publication [50], EEG data were recorded from 32 active electrodes, and by employing a spatially-coded BCI, the classification method reached an ITR of 31 \(\pm\) 17 bits/min in novice users completing the task for the first time. CNN delivers significantly better results for classification accuracy compared to other methods. During CNN training, filter weights are optimized to select useful features. The number of features processed through the fully connected layer is considerable: 128 filters \(\times\) 3 EEG channels \(\times\) 256 features per filter. This exceeds the number of features derived from selecting the 1-40 Hz frequency band, which is common in other methods. However, interpreting the function of these filters can be challenging. We can visualize the effects of these filters on the signals. Figure 8 displays a one second segment of the EEG signal from the O1 channel during a 5 Hz stimulus. Figure 9 illustrates the same signal after processing through a chosen filter from the fourth convolutional layer. Additionally, the spectra of these signals are shown, allowing for the analysis of the filter's effect. In Figure 8, the original input signal to the filter has a broad frequency spectrum, but frequencies at 5 Hz, 10 Hz, and 15 Hz are not readily distinguishable. In contrast, Figure 9 reveals that the output signal from the filter predominantly features frequencies around 5, 10, and 15 Hz, which correspond to the stimulation frequency and its harmonics. Therefore, the signal post-filtering contains frequencies potentially beneficial for the classification of SSVEPs. Figure 8: One second fragment of the EEG signal fed to the network input and the spectrum of this signal.