## 1 Introduction
The model performed with an accuracy of 70.7 \(\pm\) 10.6% in classifying the healthy subjects and those with the two neurological diseases. The specificity of classification for each disease ranged from 86 to 94% [9]. [PERSON] et al. explored the use of eight statistical features and Genetic Programming (GP) with the K-nearest neighbor (KNN) algorithm for interical spike detection, achieving a 91.75% average sensitivity and 92.99% average specificity [10]. [PERSON] et al. attempted to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNNs) and transfer learning. Several pretrained networks were fed with spectrograms of EEG signals acquired from the Temple University Hospital EEG corpus. The highest classification accuracies of 82.85% (using GoogLenet) and 88.30% (using Inception V3) were achieved using transfer learning and extract image features approach, respectively [11]. [PERSON] et al. developed a novel convolution module named \"semi-dilated convolution\" that better exploits the geometry of wavelet scalograms and non-square-shaped images. Then they proposed a neural network architecture named \"semi-dilated convolutional network (SDCN)\" that uses the aforementioned module to expand the receptive field along the long dimension (image width) while maintaining high resolution along the short dimension (image height), resulting in an average seizure prediction sensitivity of 98.90% for scalp EEG and 88.45-89.52% for invasive EEG [12]. [PERSON] et al. created the fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED) which resulted in a mean Area Under Curve (AUC) of 0.9868 (10-fold patient-wise cross-validation), while the sensitivity and specificity were 0.7952 and 0.9971, respectively [13]. [PERSON] et al. introduced a hybrid model combining generative and discriminative approaches for decoding visual information from MEG data across different subjects. They employed stacked generalization to enhance the decoding performance, demonstrating its effectiveness in handling inter-subject variability [14]. [PERSON] et al. explored the combination of MEG and MRI data to classify individuals with Mild Cognitive Impairment (MCI). The authors utilized a form of stacked generalization, referred to as \"late combination,\" where predictions from modality-specific classifiers are combined to improve the overall classification accuracy [15]. [PERSON], [PERSON] et al. proposed, among others, the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within the training data, with the aim of producing more stable classifiers [16]. The review of the aforementioned literature led us to several conclusions:  
1. The number of studies examining MEG signals is significantly smaller than the corresponding number for EEG, especially epileptic signals. 2. There is a focus on investigating advanced classification models, while references and evaluations of the effectiveness of simpler, fundamental classification models are notably scarce. 3. Due to the absence of comparative studies between advanced and basic models, it remains unclear what performance gains are achieved by their application relative to the cost (computational cost and implementation cost). Additionally, MEG as a method is relatively new and quite expensive, with limited clinical application. As a result, MEG signal samples, particularly those concerning specific categories of diseases such as epilepsy, are quite rare. Neurologists are still not familiar with recognizing MEG signals and tend to avoid incorporating them into clinical practice. All the above served as the motivations and simultaneously defined the objectives and direction of our work, which are the following:  
1. Evaluation of basic artificial neural network (ANN) models in the classification of epileptic signals. 2.