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Journals / Mühendislik Bilimleri ve Araştırmaları Dergisi / 2026 / Cilt: 8 Sayı: 2

Comparative Analysis of Channel Selection Methods for EEG-Based Familiar and Unfamiliar Face Recognition

EEG Tabanlı Tanıdık ve Tanıdık Olmayan Yüz Tanımada Kanal Seçim Yöntemlerinin Karşılaştırmalı Analizi

Fatma Kebire Bardak Özkul(BANDIRMA ONYEDI EYLUL UNIVERSITY)0000-0002-9380-2330
Feyzullah Temurtaş(OSTIM TECHNICAL UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-3158-4032
Pages
233–245
Publication Date
2026-10-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that the classification performance was enhanced with increasing of channel number.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study proposes a channel selection and machine learning-based approach for classifying EEG signals of familiar and unfamiliar faces.
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Key Findings & Data:Then, utilizing minimum redundancy maximum relevance, Fisher Score and Mutual Information approaches, separate EEG channel subsets were identified.
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Conclusion & Impact:The results showed that the classification performance was enhanced with increasing of channel number.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:EEG · Tanıdık/tanımadık yüz sınıflandırma · kanal seçimi

Abstract

This study proposes a channel selection and machine learning-based approach for classifying EEG signals of familiar and unfamiliar faces. In the first stage, time-frequency based features were obtained from EEG signals using Discrete Wavelet Transform. Then, utilizing minimum redundancy maximum relevance, Fisher Score and Mutual Information approaches, separate EEG channel subsets were identified. The chosen channel features were classified using a Support Vector Machine classifier and comparison evaluations were performed utilizing accuracy, sensitivity and specificity performance metrics. The results showed that the classification performance was enhanced with increasing of channel number. Using all channels yielded around 91% accuracy. Furthermore, channel scoring results indicated that EEG channels located in the central, parietal, and posterior regions had higher discrimination in distinguishing between familiar and unfamiliar faces. The study results demonstrate that successful familiar/unfamiliar face classification is possible with a low number of channels and contribute to the development of portable EEG-based biometric systems.

Özet

Bu çalışma, tanıdık ve yabancı yüzlerin EEG sinyallerini sınıflandırmak için kanal seçimi ve makine öğrenimine dayalı bir yaklaşım önermektedir. İlk aşamada, Ayrık Dalgacık Dönüşümü kullanılarak EEG sinyallerinden zaman-frekans tabanlı özellikler elde edilmiştir. Daha sonra, Minimum Yedeklilik Maksimum Alaka Düzeyi, Fisher Skoru ve Karşılıklı Bilgi yaklaşımları kullanılarak ayrı EEG kanal alt kümeleri belirlenmiştir. Seçilen kanal özellikleri, Destek Vektör Makinesi sınıflandırıcı kullanılarak sınıflandırılmış ve doğruluk, duyarlılık ve özgüllük performans metrikleri kullanılarak karşılaştırma değerlendirmeleri gerçeklenmiştir. Sonuçlar, sınıflandırma performansının kanal sayısının artmasıyla iyileştiğini göstermektedir. Tüm kanalların kullanılması yaklaşık %91 doğruluk sağlamıştır. Ayrıca, kanal puanlama sonuçları, merkezi, parietal ve posterior bölgelerde bulunan EEG kanallarının tanıdık ve yabancı yüzleri ayırt etmede daha yüksek ayrım gücüne sahip olduğunu göstermektedir. Çalışma sonuçları, düşük sayıda kanalla başarılı tanıdık/tanımadık yüz sınıflandırmasının mümkün olduğunu göstermekte ve taşınabilir EEG tabanlı biyometrik sistemlerin geliştirilmesine katkıda bulunmaktadır.

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Cite This Article

Özkul, F. K. B., Temurtaş, F. (2026). Comparative Analysis of Channel Selection Methods for EEG-Based Familiar and Unfamiliar Face Recognition. Mühendislik Bilimleri ve Araştırmaları Dergisi, Cilt: 8 Sayı: 2, 233–245. https://doi.org/10.46387/bjesr.1963946

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