ABSTRACT
Consumer neuroscience is a rapidly emerging field, with the ability to detect consumer attitudes and states via real-time passive technologies being highly valuable. While many studies have attempted to classify consumer emotions and perceived pleasantness of olfactory products, no known machine learning approach has yet been developed to directly predict consumer reward-based decision-making, which has greater behavioral relevance. In this proof-of-concept study, participants indicated their decision to have fragrance products repeated after fixed exposures to them. Single-trial power spectral density (PSD) and approximate entropy (ApEn) features were extracted from EEG signals recorded using a wearable device during fragrance exposures, and served as subject-independent inputs for 4 supervised learning algorithms (kNN, Linear-SVM, RBF- SVM, XGBoost). Using a cross-validation procedure, kNN yielded the best classification accuracy (77.6%) using both PSD and ApEn features. Acknowledging the challenging prospects of single-trial classification of high-order cognitive states especially with wearable EEG devices, this study is the first to demonstrate the viability of using sensor-level features towards practical objective prediction of consumer reward experience.
