Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5999-6002. doi: 10.1109/EMBC46164.2021.9631055.
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.
消费神经科学是一个快速发展的领域，通过实时被动技术来检测消费者的态度和状态具有很高的价值。虽然许多研究试图对消费者的情绪和嗅觉产品的感知愉悦度进行分类，但目前还没有开发出已知的机器学习方法来直接预测基于消费者奖励的决策，因为后者具有更大的行为相关性。在这项概念验证研究中，参与者在固定接触香味产品后表示是否希望重复使用这些产品。从佩戴式设备记录的脑电图信号中提取单试功率谱密度 (PSD) 和近似熵 (ApEn) 特征，并作为 4 种监督学习算法（kNN、线性 SVM、RBF-SVM、XGBoost）的独立输入。使用交叉验证程序，kNN 使用 PSD 和 ApEn 特征均取得了最佳的分类准确率（77.6%）。鉴于使用可穿戴式 EEG 设备对高阶认知状态进行单次分类的挑战性前景，本研究首次证明了使用传感器级特征对消费者奖励体验进行实际客观预测的可行性。
