Evaluating machine learning models in EEG-based thermal comfort studies: a comparative approach

Thermal comfort is a critical determinant of human health, productivity, and well-being in indoor environments. While numerous studies have utilised electroencephalography (EEG) to explore human physiological responses to varying thermal conditions, comprehensive analyses that synthesise the effectiveness of various machine learning (ML) approaches for interpreting EEG data remain limited. To address this gap, this study compares various EEG feature sets and ML algorithms using a single EEG dataset. The dataset consists of EEG signals collected from 40 participants exposed to two distinct thermal conditions: a baseline comfortable state and an overheating state induced by wearing heavy clothing. To this end, our objective is to investigate the most pertinent EEG signal features, such as mean power density, power spectral densities, and so on, and evaluate the performance of popular machine learning models for predicting thermal comfort. We examine classifiers including Support Vector Machines (SVM), Random Forests (RF), and various neural network configurations, comparing their efficacy in analysing EEG data. The results indicate that the LDA classifier demonstrates high accuracy when using mean power density features in each 1 Hz frequency range. The SVM classifier, utilizing power density ratios of EEG frequency bands, exhibits robustness in recall and F1 scores. Additionally, the CNN classifier effectively captures complex patterns in the EEG data, showcasing the potential of deep learning methods. These Gindings contribute to the optimization of indoor environmental controls and advance the Gield of environmental engineering by providing insights into the neurophysiological impacts of thermal conditions.