Analysis of EEG signals and its application to neuromarketing
Analysis of EEG signals and its application to neuromarketing
复制标题
DOI：
10.1007/s11042-017-4580-6
复制
发表时间：
2017-09-01
影响因子：
3.6
通讯作者：
Dogra, Debi Prosad
中科院分区：
文献类型：
--
作者：
Yadava, Mahendra;Kumar, Pradeep;Dogra, Debi Prosad
通过广告活动营销和促销各种消费品是众所周知的做法，以增加消费者的销售和意识。这基本上导致增加利润的制造单位。产品的再生产通常取决于各种事实，包括市场上的消费，评论家的评论，评级等，然而，了解消费者的决策偏好和使用无意识过程有效利用产品的行为预测被称为“神经营销”。由于其固有的潜力，这一领域正在迅速崛起。因此，在这方面的研究工作是很高的要求，但还没有达到令人满意的水平。在本文中，我们提出了一个预测建模框架，以了解消费者对电子商务产品的选择方面的“喜欢”和“不喜欢”，通过分析脑电信号。研究人员记录了不同年龄和性别的志愿者在浏览各种消费品时的脑电信号。实验在由各种消费品组成的数据集上进行。选择预测的准确性记录使用用户独立的测试方法的帮助下，隐马尔可夫模型（HMM）分类。我们观察到，预测结果是有希望的，该框架可以用于更好的商业模式。
Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer's comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called "Neuromarketing". This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of "likes" and "dislikes" by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.