## 1 Introduction
For example, during a seizure, there is often a sharp increase in activity in high frequencies (above 20 Hz) known as sharp wave or sharp wave-ripple complexes, which are among the most distinctive EEG patterns during an epileptic seizure [22; 23; 24]. It is worth mentioning that the analysis of EEG signals requires expertise and knowledge from a specialist who can interpret and decipher the characteristic EEG patterns during seizures [25]. Therefore, EEG signal analysis is one of the diagnostic tools employed in diagnosing and treating epilepsy. In their comprehensive review, [PERSON] et al. [26] provided an insightful overview of existing techniques in the field of automated epilepsy detection. These techniques employ diverse methods for analyzing EEG signals, including the time domain, frequency domain, time-frequency domain, and non-linear approaches. In another review paper, [PERSON] et al. [27] categorized seizure detection and prediction algorithms into time-domain methods, frequency-domain methods, wavelet-based methods, and methods based on empirical mode decomposition. [PERSON] et al. [28] emphasized the variability in pattern recognition techniques required for detecting epileptic seizures across different EEG datasets, owing to the distinct characteristics exhibited under diverse conditions. [PERSON] et al. [29] present generic approaches for seizure detection, with a focus on feature extraction from both ictal and interictal signals. They use established transformations and decompositions to extract statistical features from the high-frequency coefficients of the signals. In their study, [PERSON] et al. [30] utilized frequency bands, including delta, theta, alpha, beta, and gamma, for feature extraction in the classification of EEG signals. [PERSON] [31] suggests that seizure onset frequencies predominantly fall within the gamma frequency range (typically between 30 and 100 Hz). Furthermore, [PERSON] et al. [32] demonstrated the successful detection of epileptic seizures in all cases using only EEG signal variance. They compared the traditional variance-based method with various methods based on nonlinear time series analysis, entropy, logistic regression, discrete wavelet transform, and time-frequency distributions. Remarkably, the variance-based method outperforms the other methods, achieving the best result of 100% when applied to the same database. The studies conducted by [PERSON] et al. [33] and [PERSON] et al. [34] employ wavelet and Fourier transformations for feature extraction and classification in the detection of seizures within EEG signals. [PERSON] et al. explored the application of image-based seizure detection by utilizing a convolutional neural network on long-term EEG data, including epileptic seizures. The EEG data are filtered, segmented into short segments, transformed into EEG images, and classified by the convolutional neural network as either \"seizure\" or \"non-seizure\". In the study conducted by [PERSON] et al. [35], a novel three-dimensional convolutional neural network structure for automatic seizure detection was proposed. This network takes multi-channel EEG signals as inputs to provide an effective detection system. Furthermore, [PERSON] et al. [36] utilized a convolutional neural network for differentiating ictal, preictal, and interictal segments in the detection of epileptic seizures. Instead of manual feature extraction, raw EEG signals are directly used as inputs. The performance of time and frequency domain signals in detecting epileptic signals is compared based on the intracranial Freiburg and scalp CHB-MIT databases, in order to explore their potential. In their work, [PERSON] et al. [37] introduced transformers for seizure detection (TSD), a deep learning architecture based on the transformer model. The TSD leverages an encoder-decoder structure and attention mechanisms applied to recorded brain signals. [PERSON] et al. [38] showcased the capabilities of the transformer network in computing attention between input signal channels for seizure detection.