Thermal Comfort Classification Using Electroencephalogram Spectral and Microstate Features

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Abstract
This study presents an electroencephalogram (EEG)‐based framework for thermal comfort classification by examining epoch sensitivity, feature importance, and interpretability, and the integration of spectral and microstate descriptors. Ninety sessions were conducted in a climate‐controlled chamber under neutral, warm, and cool conditions. Channel‐wise relative‐power (RP) features across five frequency bands were extracted using multiple epoch lengths (2‐, 4‐, 8‐, 16‐, and 32‐s) and evaluated using three classifiers: linear Support Vector Machine (SVM), radial‐basis‐function (RBF) SVM, and Random Forest (RF). Among the tested configurations, SVM‐RBF using 16‐s epochs achieved the highest overall performance when the full‐feature set was used. When reducing feature dimensionality, RF‐based analysis identified a compact top‐15 feature subset that achieved 76.67% accuracy, comparable with full feature set. Feature importance and stability validation consistently indicated that several frontal‐temporal RP features, particularly gamma_T5, theta_T4, gamma_T4, and beta_FP1, were the most reliable contributors to thermal discrimination. Supporting this observation, interpretability analysis showed that theta_T4 was most informative for identifying neutral from warm states, whereas gamma_FP1 activity provided stronger contrast under cool conditions. Finally, combining the selected top‐15 RP features with EEG microstate temporal descriptors further improved RF accuracy to 88.57%, surpassing the SVM‐RBF baseline. Collectively, these findings demonstrate the feasibility of EEG‐based thermal comfort modelling under within‐subject settings and highlight the value of integrating spectral with microstate features, while further validation using larger populations is needed to improve cross‐subject generalizability.

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