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Deep learning-based comparative evaluation of EEG, HRV, and EDA biomarkers for personal thermal comfort prediction
Author(s)
Sahoh, Bukhoree
Wongsontham, Fatimah
Tipsavak, Apaporn
Chaithong, Paweena
Kliangkhlao, Mallika
Efendi, M. Arif
Songnuy, Theerapan
Punsawad, Yunyong
Date Issued
March 1, 2026
Type
Article
Abstract
Personal thermal comfort prediction significantly impacts health, well-being, and productivity, yet existing systems typically employ multiple physiological biomarkers without clear evidence for optimal single-modality solutions. This study presents a deep learning (DL)-based metrologically grounded framework to evaluate and compare three biomarkers—electroencephalography (EEG), heart rate variability (HRV), and electrodermal activity (EDA)—as traceable sensors under precisely controlled thermal conditions. We developed specialized signal preprocessing and feature engineering pipelines for each modality: 1) EEG spectral decomposition via Welch's power spectral density estimation across frequency bands (delta, theta, alpha, beta, and gamma); 2) HRV analysis through Fast Fourier Transform spectral estimation focusing on low-frequency to high-frequency component ratios; and 3) EDA feature extraction utilizing optimized filtering techniques to isolate skin conductance level and response characteristics. Three biomarker-specific DL architectures undergo Bayesian hyperparameter optimization, enabling equitable comparison of each modality's predictive performance. Results demonstrate that the HRV-based model achieves superior performance (F-measure = 0.95) while requiring minimal computational resources (0.78 MB memory footprint, 0.89 relative cost compared to the EEG baseline), establishing it as the optimal single-modality solution. These findings provide a paradigm for reproducible physiological measurement systems, advancing thermal comfort prediction by combining clinical-grade reliability with practical implementation benefits for clinical applications and next-generation indoor environmental control systems.
Citation
Biomedical Signal Processing and Control, 113, 2026