## 3 Methods
### Deep Learning  
Deep learning, as one of the branches of artificial intelligence, can yield better results than traditional machine learning methods in the case of epileptic seizure detection [101, 102]. There are several reasons why deep learning can be beneficial in this context [103, 104, 105]:  
* Hierarchical data representation: Deep learning allows for the automatic creation of multi-level data representations. As a result, neural networks can detect complex patterns and structures in EEG signals that may be difficult to identify using traditional methods. * Feature extraction: Deep learning can autonomously extract relevant features from input data, eliminating the need for manual feature engineering. In the case of epileptic seizure detection, neural networks can automatically identify characteristic patterns in EEG signals that are associated with seizures. * Utilization of larger datasets: Deep learning requires a large amount of training data. In the context of epileptic seizure detection, the availability of a large EEG database containing both seizure and non-seizure signals enables the training of more advanced neural networks. A larger amount of training data can contribute to improving classification effectiveness. * Adaptability: Neural networks can be flexible and adapt to changing conditions. In the case of epileptic seizure detection, EEG signals may undergo changes over time, and seizures can manifest in different forms. Deep learning allows the model to adapt to these changes and adjust to new patterns. However, it is important to note that the effectiveness of deep learning depends on the appropriate selection of network architecture, parameter optimization, and the quality of available training data. Deep learning techniques have been rapidly advancing in recent times for several reasons. In recent years, data collection has become easier and more prevalent, especially in areas such as image processing, natural language processing, and biomedical data analysis [106, 107]. Large datasets are crucial for effective deep learning as deep models are capable of extracting meaningful features and patterns from such data. The increase in computational power and the availability of advanced hardware, such as graphics processing units (GPUs) and tensor processing units (TPUs), enable accelerated training of neural networks [108]. This allows for the exploration of larger and more complex models, contributing to the development of deep learning techniques. Many advanced neural network architectures have been developed, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), which possess unique abilities for pattern recognition, sequence processing, or generating new data [109]. These advanced architectures drive the advancement of deep learning techniques and enable the solution of more complex problems. Knowledge about optimization, regularization, weight initialization, normalization, and other aspects of deep learning is continually evolving. This leads to the creation of increasingly efficient and effective deep learning techniques. Convolutional layers are the main component of CNNs [110]. Each convolutional layer consists of a set of filters (known as convolutional kernels) that are applied to the input data. These filters perform convolution operations, which involve multiplying the signal values in the input window by their corresponding filter weights and summing the results. This generates a feature map that contains information about detected patterns in the data. After the convolution operation, the results are passed through an activation function, such as a rectified linear unit (ReLU) [111]. Activation functions introduce non-linearity to the network, allowing for the modeling of more complex relationships between features. Pooling layers are used to reduce the dimensionality of the data. Max-pooling layers are commonly used, which select the maximum value within the input window and pass it forward [112].