EEG-BASED FAMILIAR AND UNFAMILIAR FACE ANALYSIS: CLASSIFICATION USING RMS FEATURES WITH ARTIFICIAL NEURAL NETWORKS AND RANDOM FORESTS

Authors : Fatma Kebire Bardak Özkul, Feyzullah Temurtaş

Pages : 765-778

Doi:10.17482/uumfd.1573758

View : 102 | Download : 171

Publication Date : 2025-12-19

Article Type : Research Paper

Abstract :In this study, the features obtained using the Root Mean Square (RMS) method in EEG-based face recognition processes were analyzed with probabilistic neural networks (PNN), multilayer perceptrons (MLP), and random forest classifiers. The results showed that the PNN model exhibited the highest performance with an accuracy rate of 95.05%. On the other hand, the MLP and Random Forest models showed lower performance with an accuracy rate of 73.34% and 78.01%, respectively. These differences may be due to the variability in EEG topographic responses among individuals and the inability of these models to generalize the differences in the data well enough. The study emphasizes the importance of considering individual neural differences in EEG-based classification systems. It suggests that more personalized models should be developed to balance these differences in the future.
Keywords : Tanıdık ve Tanıdık Olmayan Yüz, EEG, Yapay Sinir Ağları, Rastgele Orman

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