## 2 Material and Methods  
Magnetoencephalography is a neuroimaging technique that utilizes an array of sensors placed slightly above the scalp. The use of superconducting quantum interference devices (SQUIDs), which can detect and record biomagnetic fields of the order of 1 fT (=10\({}^{-15}\) T), makes MEG very sensitive to the microscopic alterations of the magnetic field produced by neural electrical activity. Thus, it achieves a very good spatial resolution (up to 5 mm), as well as a great time resolution at the scale of one millisecond or even better, which makes MEG a great tool for tracing real-time changes in brain activity and state. It can be used in combination with other imaging techniques (MRI, f-MRI, PET, PET-CT) to give a detailed 3D imaging of brain activity in specific areas. It is non-invasive and it is completely safe, causing no discomfort. Moreover, it can detect epileptic activity and spot epileptic foci in the brain activity with the patient resting in the interictal state, without inducing unpleasant and even painful seizures to the patient, caused by intermittent visual and auditory stimuli, as is the usual routine during EEG recording [19]. Epileptic activity appears in EEG and MEG as irregular patterns in the form of spikes, spikes-and-slow waves, or Sharpwaves. The morphology of spikes and sharp waves in EEG was thoroughly analyzed by [PERSON], and these waves can be used for epilepsy diagnosis [20]. Although studies are being carried out [21], there still is no formal definition of epileptic spikes in MEG. However, even if it seems an oxymoron, in comparison to EEG signals, \"MEG spike yield and localization is superior compared with EEG\" [22]. Epileptic signals in MEG have different morphological characteristics (duration, shape, and sharpness) from those in EEG. This can be explained by the small effect on the MEG signal from the interference from the skull and scalp. Furthermore, muscular activity and eye movement have much less effect on MEG [23]. Figure 1 depicts two segments of MEG signals used in the present study; the first (a) is classified as non-epileptic and the second (b) as epileptic. In (c), we see the Spectrum bar graph and the heatmap of the corresponding channel's ST-FFT of the same non-epileptic MEG segment as (a), with different axis scaling to better depict the signal's details. The signal segments were deliberately selected to exhibit significant differences, ensuring that these variations are clearly represented in the plots. It is important to note that these segments do not represent the average pair of epileptic and non-epileptic signals. For each segment, we show the Signal plot, the Spectrum bar graph and the heatmap of the corresponding signal's Short-Time Fast Fourier Transform (ST-FFT) (approximately 9 s or 2100 samples). It is important to note that significantly less research has been conducted applying deep learning models to MEG data compared to EEG data. The primary reason for this disparity is the high cost associated with SQUID is a costly device, with installation expenses that are even more substantial. Ideally, the installation requires a Faraday cage to ensure complete electromagnetic isolation for the highly sensitive SQUID sensors, along with acoustic and anti-vibration isolation. These requirements can raise the total cost to several million euros or USD. The MEG signals used in this study were recorded in the MEG Unit of the Laboratory of Medical Physics, Department of Medicine, Democritus University of Thrace, located in Alexandroupolis, Greece using Neuromag-122\({}^{\text{m}}\), Elekta Neuromag Ltd., Finland that utilizes the SQUID technology. MEG recordings were obtained from 5 epileptic patients, 3 male and 2 female, 23-40 years old, mean age 33.5 years. All 5 patients were diagnosed by special neurologists as pharmacoresistant Idiopathic Generalized Epilepsy patients, according to the International League Against Epilepsy (ILAE) classification.