## 5 Discussion
Figure 9: One second fragment of the EEG signal after applying the exemplary convolutional filter (no 110) in the 4 th layer and spectrum of this signal. Several studies on CNNs indicate that the network is more robust to artifacts [51; 52]. To determine whether the CNN approach is more effective in classifying SSVEPs with artifacts, we introduced Gaussian noise into the test signal. Gaussian noise closely approximates EMG artifacts resulting from muscular activities like jaw clenching, tongue movement, and swallowing [53]. We then attempted to classify sections of the noisy EEG signals for stimuli at 5, 6, 7, and 8 Hz. The classification accuracies for the CNN and CCA methods are listed in Table 5. Case I presents the classification accuracies (0.81 for CNN and 0.75 for CCA) obtained with the originally recorded EEG signal, which had a standard deviation of 0.87 \(\times\) 10\({}^{-5}\). In Case II, Gaussian noise was added to the EEG signal with a standard deviation of 5.99 \(\times\) 10\({}^{-6}\), leading to a decrease in classification accuracy (0.69 for CNN and 0.54 for CCA). For Case III, the noise standard deviation was significantly increased to 1.60 \(\times\) 10\({}^{-5}\), which resulted in a further reduction in classification accuracy to 0.59 for CNN and 0.45 for CCA. The augmentation of EEG data using the proposed method proved to be effective for SSVEP. This technique enables the creation of any number of training examples. However, the data augmentation method does not account for inter-channel relationships. If there are significant dependencies between channels O\({}_{1}\), O\({}_{2}\), and O\({}_{x}\)--related to phase, frequencies, or amplitudes, for instance--the method may not generate accurate data for network training. Therefore, caution is advised when applying this technique to other potentials used in BCI, such as P300 or ERD/ERS. The results we obtained align with those of other researchers who have applied CNN and deep learning to classification tasks in BCI systems. The experiment detailed in [54] involved nine flicker stimuli of different frequencies, and a CNN-based multitarget rapid classification method was constructed for nine classification tasks. The average accuracy of AR-BCI using the CNN model at a 1 s stimulus duration was about 81.83%. In [55], to enhance the classification accuracy of SSVEP signals during movement, SSVEP data were collected from five targets moving at speeds of 0 km/h, 2.5 km/h, and 5 km/h. A convolutional neural network (CNN) was developed to discern the relationship between the EEG signal and the pattern corresponding to each stimulus frequency. The proposed method outperformed traditional methods (i.e., CCA, FBCCA, and SVM) at all speeds, with CNN accuracies of 86.08%, 71.53%, and 60.63% from the lowest to highest walking speeds, respectively. In [26], the use of 64 channels yielded excellent results; however, when reduced to three channels, the classification accuracy was approximately 51% and 42% for sets of EEG signals. In [56], a BCI was utilized in an online experiment to spell the word 'SPELLER' using a 2 s time window. The system attained an average accuracy of 97.4% and an information transfer rate of 49 bpm, demonstrating the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller using a 1D CNN. The study in [57] introduced a filter bank convolutional neural network (FBCNN) approach to optimize SSVEP classification. Three filters, each covering a harmonic of the SSVEP signals, were used to extract and differentiate the relevant components, with their information transformed into the frequency domain. Experimental results indicated that FBCNN enhances the performance of CNN-based SSVEP classification methods and holds significant potential for SSVEP-based BCIs. FBCNN results were approximately 2% better than those of traditional CNNs, though a wide dispersion of results was observed for both methods, varying by individual.