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Journals / Bozok Journal of Engineering and Architecture / 2026 / Cilt: 5 Sayı: 1

Real-Time Cognitive Risk Classification Using TGAM EEG Sensor with IoT and Fuzzy Logic Integration

IoT ve Bulanık Mantık Entegrasyonuyla TGAM EEG Sensörü Kullanılarak Gerçek Zamanlı Bilişsel Risk Sınıflandırması

İbrahim Karaman(BOZOK ÜNİVERSİTESİ)0000-0001-8396-9797
Abdurrahman Öcal(YOZGAT BOZOK ÜNİVERSİTESİ)0000-0002-6960-7156
Pages
1–11
Publication Date
2026-06-29
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AI Summary & Key Takeaways

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

The results demonstrate that the proposed portable and low-cost system can effectively and reliably monitor cognitive state in work environments requiring high levels of attention.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents an IoT-based approach to classifying the cognitive risk levels of individuals working in critical fields such as education, healthcare, transportation, and security in real time.
📊
Key Findings & Data:Attention and meditation data obtained from the TGAM single-channel EEG sensor are transferred to the ThingSpeak platform via ESP32 and analyzed using a rule-based fuzzy logic system developed in MATLAB.
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Conclusion & Impact:The results demonstrate that the proposed portable and low-cost system can effectively and reliably monitor cognitive state in work environments requiring high levels of attention.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:EEG izlenmesi · Gerçek zamanlı · IoT

Abstract

This study presents an IoT-based approach to classifying the cognitive risk levels of individuals working in critical fields such as education, healthcare, transportation, and security in real time. Attention and meditation data obtained from the TGAM single-channel EEG sensor are transferred to the ThingSpeak platform via ESP32 and analyzed using a rule-based fuzzy logic system developed in MATLAB. The system classifies the individual's cognitive state into three risk levels: low, medium, and high. The results demonstrate that the proposed portable and low-cost system can effectively and reliably monitor cognitive state in work environments requiring high levels of attention.

Özet

Bu çalışma, eğitim, sağlık, ulaşım ve güvenlik gibi kritik alanlarda çalışan bireylerin bilişsel risk düzeylerini gerçek zamanlı olarak sınıflandırmaya yönelik IoT tabanlı bir yaklaşım sunmaktadır. TGAM tek kanallı EEG sensöründen elde edilen dikkat ve meditasyon verileri, ESP32 üzerinden ThingSpeak platformuna aktarılmakta ve MATLAB ortamında geliştirilen kural tabanlı bulanık mantık sistemi ile analiz edilmektedir. Sistem, bireyin bilişsel durumunu düşük, orta ve yüksek olmak üzere üç risk seviyesinde sınıflandırmaktadır. Elde edilen sonuçlar, önerilen taşınabilir ve düşük maliyetli sistemin, yüksek dikkat gerektiren çalışma ortamlarında bilişsel durumun etkin ve güvenilir biçimde izlenebileceğini göstermektedir.

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Cite This Article

Karaman, İ., Öcal, A. (2026). Real-Time Cognitive Risk Classification Using TGAM EEG Sensor with IoT and Fuzzy Logic Integration. Bozok Journal of Engineering and Architecture, Cilt: 5 Sayı: 1, 1–11. https://doi.org/10.70700/bjea.1894968

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