Deep learning-based comparative evaluation of EEG, HRV, and EDA biomarkers for personal thermal comfort prediction

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dc.contributor.authorSahoh, Bukhoree
dc.contributor.authorWongsontham, Fatimah
dc.contributor.authorTipsavak, Apaporn
dc.contributor.authorChaithong, Paweena
dc.contributor.authorKliangkhlao, Mallika
dc.contributor.authorEfendi, M. Arif
dc.contributor.authorSongnuy, Theerapan
dc.contributor.authorPunsawad, Yunyong
dc.date.accessioned2026-08-06T10:54:56Z
dc.date.available2026-08-06T10:54:56Z
dc.date.issued2026-03-01
dc.description.abstractPersonal 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.
dc.identifier.citationBiomedical Signal Processing and Control, 113, 2026
dc.identifier.doi10.1016/j.bspc.2025.108972
dc.identifier.issn17468094
dc.identifier.other2-s2.0-105019064834
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17953
dc.sourceBiomedical Signal Processing and Control
dc.subjectArtificial intelligence
dc.subjectBayesian optimization
dc.subjectBiofeedback
dc.subjectMachine learning
dc.subjectPhysiological response
dc.subjectUser-centered model
dc.titleDeep learning-based comparative evaluation of EEG, HRV, and EDA biomarkers for personal thermal comfort prediction
dc.typeArticle

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