Deciphering neural signatures of thermal perception: A fine-grained Electroencephalogram (EEG) analysis approach

• Overcoming methodological limits with a robust experimental and EEG analysis design. • Key EEG features identified: theta_O2, delta_T5, and gamma_T3 relative powers. • Introducing neural signatures to differentiate warm, neutral, and cool states. • Cognition impacts thermal perception in frontal lobe, alpha, and beta bands. Despite Electroencephalogram (EEG) potential for establishing responsive indoor environmental controls, its effectiveness in distinguishing diverse thermal states remains uncertain due to inconsistencies in prior research. This study aimed to decipher the neural basis of thermal perception by identifying the most distinctive EEG features for warm, neutral, and cool states, alongside evaluating the impact of main cognitive functions using diverse tasks. It employed a combination of within-subject design—wherein each participant underwent six distinct task types across three thermal states—with a time–frequency Power Spectral Density (PSD) EEG analysis. This integrated approach facilitated robust findings with large number of observations, and desirable statistical powers. We utilized a fine-grained spatio-temporal EEG device compatible with international 10–20 system to capture data. The EEG data analysis examined activity at each of the electrodes specified in this system across all major frequency bands. We discovered that the relative power in the theta band at the O2 scalp position, delta band at the T5 position, and gamma band at the T3 are the top-performing features that consistently distinguish the three thermal states across all participants and tasks. However, the top-performing features varied in pairwise comparisons of the thermal states. The most pronounced impacts of cognitive tasks on thermal perception were observed in the frontal lobe, particularly within the alpha and beta frequency domains. This research enhances our understanding of thermal perception, paving the way for future studies to refine indoor environment optimization using real-time neural feedback.