## 4 Results  
Classification accuracy was used to evaluate the performance of individual classification methods. This measure is commonly used to assess classifiers and the effectiveness of BCI systems. The classification accuracy for each classifier was determined based on the last 10 s of real recorded EEG signals. During testing, 1 s windows overlapping by 0.5 s were employed, resulting in 72 windows for four stimulation frequency classes: 5, 6, 7, and 8 Hz. Table 3 shows the accuracy and macro average F1-score results obtained for the \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Execution Time** \\ \hline Data set augmentation & 12.115 s \\ \hline CCA algorithm for classification (does not require training) & 0.112 s \\ \hline sMP algorithm for classification (does not require training) & 0.311 s \\ \hline Training the CNN for 50 epochs & 145 min 8 s \\ \hline Training the MLP & 11.1 s \\ \hline Training the LDA & 14.3 s \\ \hline Training the QDA & 17.2 s \\ \hline Training LDA with SFS/\(t\)-test feature selection & 31.4 s/19.2 s \\ \hline Training QDA with SFS/\(t\)-test feature selection & 41.8 s/22.5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution times of the individual algorithms. individual classification methods on the test set. Macro average F1-score provides a balanced assessment of precision and sensitivity. In addition to the methods' symbolic names (CNN, CCA, sMP, MLP, QDA, LDA, QDA-SFS, LDA-SFS, QDA-T, LDA-T), details about the data used at the classifiers' input (EEG raw, DFT 1-40 Hz, DFT specific frequencies) and selection methods (SFS with 25 features, _t_-test with 14 features) are included. The table also indicates whether data augmentation was used for training (Y) or if it was the first 20 s of the recorded EEG signal (N). The highest mean classification accuracy was achieved with the CNN at 0.72. A lower average accuracy of 0.57 was observed for both CCA and MLP. The sMP method yielded slightly inferior results, with an average classification accuracy of 0.51. Standard machine learning methods that employ spectral features and feature selection achieved classification accuracies ranging from 0.51 to 0.54. It is important to note that the classification pertained to 1 s windows across four classes. The random operation of a four-class classifier would result in a classification accuracy of 0.25. Therefore, it can be concluded that the methods under consideration deliver satisfactory results that are practically applicable. Attention should also be given to the variations in classifier accuracies among individual users. These differences can be attributed to the psychophysical characteristics of the person being tested and are a normal phenomenon. Additionally, some individuals are more naturally inclined to generate SSVEP responses to visual stimuli. To determine if the comparison of classification algorithms across five subjects (S01-S05) is reliable, statistical tests were conducted. Given the small sample size and the uncertain distribution of results, the non-parametric Wilcoxon-Mann-Whitney test was utilized [48]. \(p\)-values were calculated from a two-sided Wilcoxon signed-rank test. The classification accuracy results for the CNN method compared with other methods used by us (QDA, LDA, QDA-SFS, LDA-SFS, QDA-T, LDA-T, CCA and sMP) were found to be statistically significant at  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **CNN** & **CCA** & **sMP** & **MLP** & **QDA** & **LDA** & **QDA-SFS** & **LDA-SFS** & **QDA-T** & **LDA-T** \\ \hline \multirow{2}{*}{Input} & \multirow{2}{*}{EEG raw} & DFT & DFT & \multirow{2}{*}{ \begin{tabular}{c} 1-40 Hz \\ Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{ \begin{tabular}{c} 1-40 Hz \\ Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{ \begin{tabular}{c} 1-40 Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{ \begin{tabular}{c} 1-40 Hz \\ Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{ \begin{tabular}{c} 1-40 Hz \\ \end{tabular}  } & DFT & \multirow{2}{*}{