论文标题
智能信号的智能分析以评估消费者决策：关于神经营销的研究
Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing
论文作者
论文摘要
神经营销是一个新兴领域，结合了神经科学和营销，以了解影响消费者决策的因素。该研究提出了一种通过分析脑电图（EEG）信号来了解消费者对广告（AD）和产品的积极和负面反应的方法。这些信号是使用属于18-22岁的志愿者的低成本单电极耳机记录的。采用机器学习方法（NB），支持向量机（SVM），K-Nearest邻居和决策树以及拟议的深度学习（DL）模型进行了详细的主题（SD）和主题独立分析（SI）分析。 SVM和NB的精度为0.63，用于SD分析。在SI分析中，SVM在广告，产品和基于性别的分析方面表现更好。此外，DL模型的性能与SVM的性能相当，尤其是在基于产品和ADS的分析中。
Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and negative reactions to advertisements (ads) and products by analysing electroencephalogram (EEG) signals. These signals are recorded using a low-cost single electrode headset from volunteers belonging to the ages 18-22. A detailed subject dependent (SD) and subject independent (SI) analysis was performed employing machine learning methods like Naive Bayes (NB), Support Vector Machine (SVM), k-nearest neighbour and Decision Tree and the proposed deep learning (DL) model. SVM and NB yielded an accuracy (Acc.) of 0.63 for the SD analysis. In SI analysis, SVM performed better for the advertisement, product and gender-based analysis. Furthermore, the performance of the DL model was on par with that of SVM, especially, in product and ads-based analysis.
