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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 16 Sayı: 3

Evaluation of Time-Frequency Based Features in Emotion Classification with EEG Using Machine Learning Methods

EEG ile Duygu Sınıflandırmasında Zaman-Frekans Tabanlı Özniteliklerin Makine Öğrenmesi Yöntemleriyle Değerlendirilmesi

Büşra Tuğçe Susam(HAKKARİ ÜNİVERSİTESİ)0000-0001-8250-2044
Mehmet Salih Kurt(HAKKARİ ÜNİVERSİTESİ)0000-0002-0345-4599
Erkan Yaşar(HAKKARİ ÜNİVERSİTESİ)0000-0001-7333-6320
Yusuf Yargı Baydilli(HAKKARİ ÜNİVERSİTESİ)0000-0002-4457-2081
Pages
870–888
Publication Date
2026-09-01
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AI Summary & Key Takeaways

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

The findings indicate that combining time- and frequency-based features not only yields high classification accuracy but also ensures strong inter-subject generalizability in EEG-based emotion recognition systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electroencephalography (EEG) signals are a widely used neurophysiological method for analyzing human mental and emotional states.
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Key Findings & Data:EEG-based emotion recognition systems have enabled significant advancements in human-computer interaction and healthcare technologies.
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Conclusion & Impact:The findings indicate that combining time- and frequency-based features not only yields high classification accuracy but also ensures strong inter-subject generalizability in EEG-based emotion recognition systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:• Duygu tanıma · • Makine öğrenmesi · • Zaman-frekans özellikleri

Abstract

Electroencephalography (EEG) signals are a widely used neurophysiological method for analyzing human mental and emotional states. EEG-based emotion recognition systems have enabled significant advancements in human-computer interaction and healthcare technologies. However, there is a lack of studies in the literature that integrate different types of features and assess their generalizability across individuals. Therefore, this study aims to compare the performance of various machine learning models using different validation methods, by addressing feature extraction, selection, and evaluation in a comprehensive manner. In our study, we extracted time and frequency components from 14-channel EEG signals recorded from 28 participants in the GAMEEMO dataset. The most informative features were then selected using the ANOVA method and interpreted at both the time and frequency levels. During the classification phase, we addressed both binary (positive/negative emotion) and multiclass (boring, calm, scary, fun) emotion recognition tasks. Various machine learning algorithms were compared using 5-fold cross-validation (CV) and leave-one-subject-out cross-validation (LOSOCV) strategies. In the binary emotion recognition task, the highest classification accuracy achieved was 94.70% under 5-fold CV and 96.43% under the LOSOCV scenario. In the multiclass emotion recognition task, the highest accuracy reached 96.44% using 5-fold CV and 98.21% under the LOSOCV strategy. The findings indicate that combining time- and frequency-based features not only yields high classification accuracy but also ensures strong inter-subject generalizability in EEG-based emotion recognition systems.

Özet

Elektroansefalografi (EEG) sinyalleri insanların zihinsel ve duygusal durumlarını analiz etmede yaygın olarak kullanılan nörofizyolojik bir ölçüm yöntemidir. EEG tabanlı duygu tanıma sistemleri sayesinde insan-bilgisayar etkileşimi ve sağlık teknolojileri alanlarında önemli bulguların elde edilmesi mümkün olmuştur. Fakat literatürde farklı türdeki özniteliklerin birlikte kullanıldığı ve bireyler arası genellenebilirliğin değerlendirildiği çalışmalar oldukça sınırlıdır. Bu nedenle bu çalışmada öznitelik çıkarımı, seçimi ve değerlendirmesi bütüncül biçimde ele alınarak, farklı doğrulama yöntemleriyle çeşitli makine öğrenmesi modellerinin performanslarının karşılaştırılması amaçlanmıştır. Çalışmamızda GAMEEMO veri setinde yer alan 28 katılımcıya ait 14 kanallı EEG sinyallerinden, zaman ve frekans bileşenlerinin çıkarımı gerçekleştirilmiştir. Sonrasında ise, ANOVA yöntemi ile en etkili öznitelikler seçilerek, özniteliklerin frekans ve zaman düzeyinde yorumlanması sağlanmıştır. Sınıflandırma aşamasında, hem ikili (pozitif/negatif duygu) hem de çok sınıflı (Sıkıcı – Sakin – Korkunç – Eğlenceli) duygu sınıflandırma görevleri ele alınmış; çeşitli makine öğrenmesi algoritmaları hem 5 katlı çapraz doğrulama (5-fold CV) hem de birey-dışı bırakmalı çapraz doğrulama (LOSOCV) stratejileri altında karşılaştırılmıştır. İkili duygu sınıflandırma görevinde, 5-fold CV altında en yüksek sınıflandırma doğruluğu %94.70, LOSOCV senaryosunda ise %96.43 olarak elde edilmiştir. Çoklu duygu sınıflandırma görevinde, 5-fold CV yöntemiyle en yüksek doğruluk %96.44, LOSOCV stratejisi altında ise %98.21 olarak ölçülmüştür. Çalışma sonunda, zaman ve frekans temelli özniteliklerin birlikte kullanılmasının EEG tabanlı duygu tanıma sistemlerinde yalnızca yüksek sınıflandırma doğruluğu değil, aynı zamanda güçlü bireyler arası genellenebilirlik sunduğu görülmüştür.

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Cite This Article

Susam, B. T., Kurt, M. S., Yaşar, E., Baydilli, Y. Y. (2026). Evaluation of Time-Frequency Based Features in Emotion Classification with EEG Using Machine Learning Methods. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 16 Sayı: 3, 870–888. https://doi.org/10.21597/jist.1800619

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