## 1 Introduction  
Brain-computer interfaces (BCI) have been continuously developing over twelve years. They enable communication for completely paralyzed people, but at the same time they are increasingly being used by healthy individuals, for example in the entertainment industry [1; 2; 3; 4; 5]. BCI employs several EEG potentials. The most common are brain potentials associated with movement (ERD/ERS), P300 potentials, and steady-state visually evoked potentials (SSVEP) [6; 7]. SSVEP-based BCIs are relatively common because they are easy to use. They require the user to observe flashing lights at a given frequency. The stimulators can be specially constructed panels with LEDs or LCD screens [8; 9; 10]. SSVEPs appear in the back of the head, where the visual cortex is located [11]. Many SSVEP-based interfaces utilize a limited number of electrodes, typically positioned over the visual cortex at the back of the head, with O\({}_{1}\), O\({}_{2}\), and O\({}_{\text{z}}\) being the most commonly used [12]. We can observe the dominance of brain waves with the same frequencies as stimuli and their harmonics in the visual cortex. Power spectral density analysis (PSDA) methods [13] are the most widely used for feature extraction to distinguish stimulation frequencies. Dedicated methods have also been developed, such as canonical correlation analysis (CCA) [14] or simplified matching pursuit (sMP) [15]. Typically, a calibration session is performed in BCI systems to train the classifier to detect specific patterns. These patterns may differ for each person and each EEG signal registration. For example, each user may have slightly different SSVEPs (amplitudes). This may be due to anatomical and physiological differences (thickness of the skull, properties of head skin, structure of the cerebral cortex). Differences in the registration of SSVEPs may appear even for the same person (different electrode placements, skin contact surface with the electrode, stimulus power). In a calibration session, a user observes the known stimulation frequencies. The recorded EEG signal for a given stimulation frequency allows for the extraction of features to train the system. BCI can also run without a calibration session. In this case, we analyze the stimulation frequencies and their harmonics in the EEG signal. This simplification, however, results in lower efficiency of the system [16; 17]. Features for SSVEP-based BCIs may encompass specific frequencies and their harmonics [18]. These features are utilized to train the classification and decision-making systems. For the SSVEP interface, numerous standard machine learning techniques are employed, including k-nearest neighbors (K-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVM), and multilayer perceptron (MLP), among others [19]. Additionally, deep learning techniques are used for this purpose, with convolutional neural networks (CNN), long short-term memory networks (LSTM), and autoencoders (AE) being the most common structures [20]. Deep learning techniques offer several benefits, such as improved classification results and the capability for automatic feature extraction from signals and images, as seen with CNNs [21]. However, the disadvantages of deep learning are notable, including the necessity for a large dataset for training and the extensive time required for network training [22]. Given that deep learning techniques demand substantial training data, the development of effective methods for augmenting EEG data recorded during calibration sessions presents a significant challenge. In recent years, numerous solutions employing convolutional neural networks (CNNs) for SSVEP detection have been developed. The study referenced in [23], discusses a machine learning approach for detecting SSVEP using a minimal number of channels.