## 1 Introduction
In [24], a proposed CNN model is compared with a standard neural network and other leading methods for SSVEP decoding--such as canonical correlation analysis (CCA), a CCA-based classifier, a multivariate synchronization index, and CCA combined with a k-nearest neighbors (K-NN) classifier--in an offline analysis. The research in [25] introduces a fusion algorithm (CCA-CWT-SVM) that integrates CCA, continuous wavelet transform (CWT), and support vector machine (SVM) to enhance classification accuracy for targetless stimuli when a single feature extraction method is used. In [26], a novel deep neural network (DNN) architecture is presented that processes multi-channel SSVEP signals by convolving across sub-bands of harmonics, channels, time, and classifies the signals at the fully connected layer. In [27], a classification method based on a convolutional neural network (CNN) was presented to enhance the detection accuracy of SSVEP amid competing stimuli. The method was evaluated using a seven-class SSVEP dataset from ten healthy participants. The study in [28] demonstrates the use of a compact convolutional neural network (Compact-CNN), which requires only raw EEG signals for automatic feature extraction, in decoding signals from a 12-class SSVEP dataset without user-specific calibration. In [29], a nonlinear model based on a convolutional neural network, named convolutional correlation analysis (Conv-CA), was introduced. Unlike pure deep learning models, Conv-CA combines a CNN with a unique correlation layer, where the CNN transforms multiple EEG channels into a single signal, and the correlation layer computes the correlation coefficients between this transformed signal and the reference signals. In [30], a complex-valued convolutional neural network (CVCNN) is proposed to overcome the limitation of SSVEP-based BCIs, which is the available stimulation frequency. The presented results demonstrate that the proposed method not only overcomes the limitation of the stimulation frequency but also outperforms conventional SSVEP feature extraction methods. Articles [31; 32] introduce a convolutional neural network (CNN) specifically designed to learn the relationship between EEG signals and the templates corresponding to each stimulus frequency of SSVEPs. The effectiveness of the proposed method is validated by comparison with the standard canonical correlation analysis (CCA) and other state-of-the-art methods for decoding SSVEPs (i.e., CNN and task-related component analysis, TRCA, Vaughan, ON, Canada) using actual SSVEP datasets. The study confirmed the efficiency of the proposed CNN-based network in decoding SSVEPs. A comprehensive list of various algorithms used for SSVEP classification, along with signal recording methods, number of channels, number of users, and classification accuracy, is available in the work cited as [23]. The analysis of the literature indicates that the use of convolutional neural networks allows for satisfactory SSVEP recognition accuracy. However, the practical application of CNNs has been investigated only on a limited basis. This limitation pertains to issues such as the small number of electrodes, extended training times for CNNs, the application of transfer learning techniques, and the effectiveness of CNNs for user-independent classification. A particularly significant challenge in practical CNN application for BCI systems is the limited size of training sets. Typically, the training (calibration) session is brief and includes only a few examples. While such a limited dataset suffices for classical machine learning algorithms, CNNs require many more training examples. Data augmentation (DA) strategies are beneficial in this context. There are numerous data augmentation techniques, primarily developed for image processing, which include geometric transformations, flipping, cropping, rotation, photometric and color transformations, and noise injection [33]. However, techniques used for augmenting image data are not directly transferable to EEG data augmentation.