## 5 Conclusions  
In this study, we applied simple neural network models for classifying MEG signal data from epileptic patients. Our findings demonstrate that it is possible to achieve a high classification accuracy of up to 92% in distinguishing signals with epileptic activity from those without, even when using basic feed-forward neural networks (FFNNs). However, this comes at the cost of prolonged training times. This computational cost can be significantly reduced by employing one-dimensional convolutional neural networks (1D-CNNs), with only a slight reduction in classification accuracy.  
Furthermore, our experimental results indicate that incorporating variations of stacked generalization can enhance classification accuracy by more than 1%, without significantly increasing computational overhead. Although the use of Transfer Learning-stacked generalization for MEG signal classification is not novel [17, 18], our study shows the applicability of these particular configurations on MEG data for the classification of epileptic signals.  
Beyond its technical contributions, our work has potential practical applications in both educational and clinical settings. The proposed classification method could assist specialized neurologists in learning to recognize MEG signal patterns, ultimately supporting the diagnosis and understanding of epileptic activity.  
For future work, we plan to explore the capabilities of two-dimensional convolutional neural networks (2D-CNNs) applied to the two-dimensional representations of the Short-Time Fast Fourier Transform (ST-FFT) of MEG signals. These results will be compared against those obtained from CNNs and deep convolutional networks, such as Inception V3, applied to FFT-transformed MEG images. Additionally, further optimization of our models may be achieved by integrating ensemble learning techniques across the best-performing models trained on different data representations (raw MEG signals and their FFT spectrum). Finally, we aim to introduce recursive methodologies and implement Genetic Algorithms (GAs) to optimize the architectures of FFNNs, 1D-CNNs, and 2D-CNNs.  
Conceptualization, A.A. and A.S.; methodology, A.S.; software, A.S.; validation, A.A., A.S. and A.K.; formal analysis, A.A. and A.T.; investigation, A.S.; resources, A.S.; data curation, A.A. and A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.A., A.S., A.K. and A.T.; visualization, A.S.; supervision, A.A.; project administration, A.A.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of Democitus University of Thrace,Greece (protocol code AII\(\Theta\)/EHAE/39929/276 and date of approval/29 March 2023) for studies involving humans.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** Authors would like to cordially thank [PERSON], Director of Laboratory of Applied Mathematics, Hellenic Open University, Patras, Greece, for her valuable help, by providing access to the Computer Unit infrastructure.  
**Conflicts of Interest:** The authors declare no conflicts of interest.