Research Article
Characterizing Epilepsy-Related EEG Frequency Patterns Using Self-Organizing Maps
Hazem Doufesh*
Issue:
Volume 14, Issue 3, September 2026
Pages:
60-67
Received:
29 June 2026
Accepted:
10 July 2026
Published:
28 July 2026
DOI:
10.11648/j.ijbse.20261403.11
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Abstract: Understanding the effects of epilepsy on oscillatory dynamics in the human brain is essential to improve the neurophysiological characterization of epileptic disorders. Self-Organizing Map (SOM) is an unsupervised artificial neural network technique that provides a powerful tool to visualize and explore complex high-dimensional EEG data without the need for predefined diagnostic labels. Thirty patients with confirmed epilepsy underwent continuous EEG recording using a computer-based data acquisition system (Nicolet NicVue). Electrode data were recorded from four scalp locations frontal (F3), central (C3), parietal (P3), and occipital (O1). Power spectral density was calculated using Fast Fourier Transform to extract the five canonical EEG frequency bands: delta, theta, alpha, beta, and gamma. Four SOM maps were trained and optimized, and correlation patterns between epileptic brain activity and each frequency band were identified through U-matrix visualization and component plane analysis. The visualized results indicated that the theta band showed the strongest positive correlation with epileptic activity, while the delta band exhibited the weakest association. Notably, no significant correlation with epilepsy was found for the alpha, beta and gamma bands at any of the recorded electrode sites. The SOM based analysis offers an interpretable and computationally efficient framework for exploring associations of EEG frequency bands in epilepsy. Consistent increase in theta-band activity across cortical regions supports its role as a candidate neurophysiological marker of epileptic brain states.
Abstract: Understanding the effects of epilepsy on oscillatory dynamics in the human brain is essential to improve the neurophysiological characterization of epileptic disorders. Self-Organizing Map (SOM) is an unsupervised artificial neural network technique that provides a powerful tool to visualize and explore complex high-dimensional EEG data without the...
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