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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2026 / Cilt: 9 Sayı: 2026

Wavelet Decomposition and Learning Architectures for EEG-Based Lie Detection: A LOSOCV-Based Comparative Evaluation of DWT Feature Representations on the LieWaves Dataset

EEG Tabanlı Yalan Algılamada Dalgacık Ayrıştırma ve Öğrenme Mimarileri: LieWaves Veri Kümesi Üzerinde DWT Özellik Temsillerinin LOSOCV Tabanlı Karşılaştırmalı Değerlendirmesi

Rabia Cansel Uğur(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0009-0008-6037-3219
Uğur Yüzgeç(Bilecik Şeyh Edebali Üniversitesi)0000-0002-5364-6265
Pages
1–21
Publication Date
2026-09-15
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AI Summary & Key Takeaways

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

These results highlight the importance of subject-independent validation for highly overlapping EEG segments and indicate that the proposed pipeline should be regarded as an exploratory comparative framework rather than a ready-to-use practical or forensic lie detection system.

📌 3 Key Takeaways

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Objective & Hypothesis:This study systematically evaluates discrete wavelet transform (DWT)-based EEG feature representations and machine/deep learning classifiers for subject-independent deception detection using the publicly available LieWaves dataset.
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Key Findings & Data:Four configurations (db4-Level4, db4-Level5, db5-Level4, and db5-Level5) were compared within an outer Leave-One-Subject-Out Cross-Validation (LOSOCV) framework using a three-subject, subject-wise inner holdout procedure; db5-Level5 achieved the highest mean inner-validation performance.
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Conclusion & Impact:These results highlight the importance of subject-independent validation for highly overlapping EEG segments and indicate that the proposed pipeline should be regarded as an exploratory comparative framework rather than a ready-to-use practical or forensic lie detection system.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:EEG · Yalan Tespiti · Makine Öğrenmesi

Abstract

This study systematically evaluates discrete wavelet transform (DWT)-based EEG feature representations and machine/deep learning classifiers for subject-independent deception detection using the publicly available LieWaves dataset. EEG signals were bandpass filtered, processed with Automatic and Tunable Artifact Removal (ATAR), segmented with the Overlapping Sliding Window (OSW) method, and decomposed using DWT. Four configurations (db4-Level4, db4-Level5, db5-Level4, and db5-Level5) were compared within an outer Leave-One-Subject-Out Cross-Validation (LOSOCV) framework using a three-subject, subject-wise inner holdout procedure; db5-Level5 achieved the highest mean inner-validation performance. Using the fixed db5-Level5 representation, DWT-derived statistical features were evaluated with an EEGNet-inspired compact convolutional model, CNN, LSTM, BiLSTM, GRU, Random Forest, KNN, and SVM under LOSOCV. The EEGNet-inspired model achieved the highest average performance, with an accuracy of 0.5779±0.0903 and a macro F1-score of 0.5295±0.1359. Nevertheless, all models showed limited subject-independent performance. These results highlight the importance of subject-independent validation for highly overlapping EEG segments and indicate that the proposed pipeline should be regarded as an exploratory comparative framework rather than a ready-to-use practical or forensic lie detection system.

Özet

Bu çalışma, kamuya açık LieWaves veri setini kullanarak denek bağımsız EEG tabanlı yalan tespiti için Ayrık Dalgacık Dönüşümü (DWT) tabanlı özellik temsillerini ve makine/derin öğrenme sınıflandırıcılarını sistematik olarak değerlendirmektedir. EEG sinyalleri bant geçiren filtreleme ve Otomatik ve Ayarlanabilir Artefakt Giderme (ATAR) işlemlerinden geçirilmiş, Örtüşen Kayar Pencere (OSW) yöntemiyle bölütlenmiş ve DWT ile ayrıştırılmıştır. Dört DWT konfigürasyonu (db4-Level4, db4-Level5, db5-Level4 ve db5-Level5), dış Bir Denek Dışarıda Bırakma Çapraz Doğrulama (LOSOCV) çerçevesinde üç denekli, denek bazlı iç holdout doğrulaması kullanılarak karşılaştırılmış; db5-Level5 en yüksek ortalama iç doğrulama performansına ulaşmıştır. Sabit db5-Level5 temsili kullanılarak DWT tabanlı istatistiksel özellikler EEGNet'ten esinlenen kompakt konvolüsyonel model, CNN, LSTM, BiLSTM, GRU, Rastgele Orman, KNN ve SVM ile LOSOCV altında değerlendirilmiştir. EEGNet'ten esinlenen model 0,5779±0,0903 doğruluk ve 0,5295±0,1359 makro F1 puanı ile en yüksek ortalama performansı elde etmiştir. Bununla birlikte, tüm modellerin denek bağımsız performansı sınırlı kalmıştır. Sonuçlar, yüksek ölçüde örtüşen EEG bölütlerinde denek bağımsız doğrulamanın önemini göstermekte ve önerilen iş akışının kullanıma hazır pratik/adli bir sistemden ziyade keşif amaçlı karşılaştırmalı bir çerçeve olarak değerlendirilmesi gerektiğini ortaya koymaktadır.

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Cite This Article

Uğur, R. C., Yüzgeç, U. (2026). Wavelet Decomposition and Learning Architectures for EEG-Based Lie Detection: A LOSOCV-Based Comparative Evaluation of DWT Feature Representations on the LieWaves Dataset. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 9 Sayı: 2026, 1–21. https://doi.org/10.38016/jista.1901082

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