Comparative Evaluation and Optimization of Neural Networks for Epileptic Magnetoencephalogram Classification

The primary objective of this study is to evaluate and compare the classification performance of feed-forward neural networks (FFNNs) and one-dimensional convolutional neural networks (1D-CNNs) on magnetoencephalography (MEG) signals from epileptic patients. MEG signals were recorded using the NEUROMAG-122 whole-brain superconducting quantum interference device (SQUID), installed, and operated in our laboratory. The dataset comprised over 5000 MEG segments, each one with a duration of 1 s and sampled at a frequency of 256 Hz. Each segment was classified by expert neurologists as either epileptic or non-epileptic. The FFNN with five hidden layers demonstrated promising results, achieving a classification accuracy of approximately 92%. The 1D-CNN, utilizing four layers, achieved an accuracy of 90.4%, with a significantly reduced training time. Building on these findings, the study’s secondary objective was to enhance the artificial neural network (ANN) model by incorporating transfer learning–stacked generalization for FFNN in various configurations. These enhancements successfully improved the performance of the pretrained network by approximately 1%.