Estimation of HVAC Sound Preferences from Cortical Magnetic Patterns During Paired-Comparison Tasks

Assessing preferences for heating, ventilation, and air conditioning (HVAC) sounds is important for improving comfort in living spaces. Recently, preference assessments using neurophysiological measurements have gained attention. However, associations between HVAC sound preferences and cortical activity remain insufficiently understood to establish neurophysiological indices. In this study, we developed machine learning models that estimate preference scores from magnetoencephalographic (MEG) signals recorded during HVAC sound presentation. We also developed spatial filters based on the common spatial pattern to extract MEG signals associated with the preferences. Both were trained for each participant using MEG signal pairs and participant’s paired-comparison judgments of HVAC sounds based on either coolness or preference. The preference scores estimated from the training data were strongly correlated with the average preference scores obtained through a psychological paired-comparison method (r > 0.98). Analysis of trained linear models revealed that the spatial filters primarily contributing to score estimation extracted theta (4–8 Hz) and alpha (8–13 Hz) oscillations. These suggest that the signals extracted by the spatial filters may reflect cortical activity associated with the coolness and preference of HVAC sounds, and that the preference estimation models may capture the relationship between cortical activity and psychological scales of HVAC sound preferences.