EEG Tabanlı Tanıdık ve Tanıdık Olmayan Yüz Analizi: RMS Özellikleri ile Yapay Sinir Ağları ve Rastgele Orman Sınıflandırma

Year 2025, Volume: 30 Issue: 3, 765 - 778, 19.12.2025

Fatma Kebire Bardak Özkul , Feyzullah Temurtaş

https://doi.org/10.17482/uumfd.1573758

Abstract

Bu çalışmada, EEG tabanlı yüz tanıma işlemlerinde Kök Ortalama Kare (RMS) yöntemi kullanılarak elde edilen özellikler olasılıksal sinir ağları (PNN), çok katmanlı algılayıcılar (MLP) ve rastgele orman sınıflandırıcıları ile analiz edilmiştir. Sonuçlar, PNN modelinin %95.05 doğruluk oranıyla en yüksek performansı sergilediğini göstermiştir. Öte yandan, MLP ve Rastgele Orman modelleri sırasıyla %73.34 ve %78.01 doğruluk oranıyla daha düşük performans göstermiştir. Bu farklılıklar, bireyler arasındaki EEG topografik tepkilerinin değişkenliğinden ve bu modellerin verilerdeki farklılıkları yeterince iyi genelleştirememesinden kaynaklanıyor olabilir. Çalışma, EEG tabanlı sınıflandırma sistemlerinde bireysel sinirsel farklılıkların dikkate alınmasının önemini vurgulamaktadır. Gelecekte bu farklılıkları dengelemek için daha kişiselleştirilmiş modellerin geliştirilmesi gerektiğini önermektedir.

Keywords

Tanıdık ve Tanıdık Olmayan Yüz , EEG , Yapay Sinir Ağları , Rastgele Orman

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

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

Familiar and Unfamiliar Face , EEG , Artificial Neural Networks , Random Forest

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