Publications by authors named "Bangbei Tang"
Article Synopsis
- The study investigates how drivers' odor preferences can be assessed using autonomic response signals to improve driving comfort.
- Six machine learning models were developed to classify these preferences, utilizing a dataset of 132 samples from 33 drivers, focusing on physiological signals like heart rate and skin response.
- Results show that the decision tree model performed best, achieving an 88% classification accuracy, indicating that processing physiological data can enhance the understanding of drivers' olfactory preferences.
Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals.
View Article and Find Full Text PDFThe effect of odor exposure time on olfactory cognitive processing: An ERP study.
J Integr Neurosci
March 2019
The present study aimed to investigate the effects of stimulus time duration on central nervous odor processing. Twenty-one young healthy males participate in our study. There are three odor mixtures in this study and every odor mixture has two different duration time (300 ms; 500 ms).
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