Application of wearable EEG sensors for indoor thermal comfort measurements

Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement uncertainties. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different indoor temperatures. Twenty-three participants were enrolled, and the EEG signals were recorded at three ambient temperatures: cold (16 °C), neutral (24 °C), and warm (31 °C). Then, the analysis of brain Power Spectral Densities (PSDs) was performed, to investigate features correlated with thermal sensations. Statistically significant differences of several EEG features, measured on both frontal and temporal electrodes, were found between the three thermal conditions. Results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to thermal comfort measurement, but only after a dedicated signal processing to remove the uncertainty due to artifacts.

Explorations of A Real-Time VR Emotion Prediction System Using Wearable Brain-Computer Interfacing

The following research describes the potential of using a four-class emotion classification using a four-channel wearable EEG headset combined with VR for evoking emotions from each individual. Multiple researchers have conducted and established emotion recognition by using a 2-D monitor screen for stimulus responses but this introduces artifacts such as the lack of concentration on-screen or external noise disturbance and the bulky and cumbersome wires on an EEG device were difficult and time-consuming to set up thus restricting to only the trained professionals to operate this complex and sensitive medical equipment. Therefore, using a small and portable EEG headset where it was accessible for consumers was used for the brainwave signal collection. The wearable EEG headset collects the brainwave samples at 256Hz at specific locations of the brain (Tp9, Tp10, AF7, AF8) and samples were transformed via FFT to obtain the five bands (Delta, Theta, Alpha, Beta, Gamma) and were classified using random forest classifier. An emotion prediction system was then developed and the trained model was used to benchmark the 4-class emotion prediction accuracy from each individual using a 4-channel low-cost EEG headset. Subsequently, a real-time prediction system was implemented and tested. Early findings showed that it could achieve predictions as high as 76.50% for intra-subject classification results.