利用脑电图信号检测消费者偏好

Burak Ceylan, Serkan Tuzun, A. Akan
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引用次数: 2

摘要

本研究开发了一种基于脑电图(EEG)信号的喜好估计系统,用于神经营销应用。利用机器学习方法确定消费者对产品的欣赏程度已经成为一个重要的研究课题。在观看产品图片或视频时记录生物数据,然后通过信号处理方法进行处理。本研究记录了被试观看两段不同的汽车广告视频的32个通道脑电图信号,并确定其喜欢状态。在观看完广告视频后,参与者被要求对产品的不同图像(前视图、仪表板、侧视图、后视图、尾灯、标志和格栅)进行投票。利用经验模态分解(EMD)和集合经验模态分解(EEMD)对不同视频区域对应的脑电信号进行分割和分析。从本征模态函数(IMF)中提取统计特征,并进行喜好状态分类。基于EMD和eemd的方法在Brand1上的分类性能分别为93.4%和97.8%,在Brand2上的分类性能分别为93.5%和97.4%。此外,两个品牌的分类准确率分别为85.1%和85.7%。支持向量机(SVM)的结果表明,基于脑电图的方法可用于神经营销研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Consumer Preferences Using EEG Signals
In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD- and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.
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