Abstract
Human brain studies have shown that olfactory perception can regulate emotion and attention networks and prevent depressed mental states. Fragrance diffusers have been used as the potential appliance to reconcile mental conditions and achieve stress relief in daily life. Although perceiving fragrances are a complicated and subjective experience, studies have shown that it is possible to reveal the personal preference of fragrance from brain activity measured by electroencephalography (EEG). Moreover, using EEG to detect neural/mental states and apply them to human-machine interfaces has also been investigated for years. Therefore, this pilot study has two aims: (1) to identify users' preference for fragrances from EEG; (2) to develop a personalized fragrance diffuser, Aroma Box, which can detect three mental states from EEG when a user feels depressed, stressed, or drowsily and then release fragrances in real-time to help user recover from the abnormal states. To achieve this goal, we first extracted the features and built a classifier to identify the user's preference for fragrances from EEG. Then we calculated the indicators of brain states based on the EEG frequency analysis. Based on our preliminary experimental results, we deployed our algorithms in an in-house developed diffuser with a consumer 32-channel EEG headset, which has been further implemented in a real-life working environment and evaluated its efficacy by two users.