Читать книгу Machine Learning for Healthcare Applications - Группа авторов - Страница 65

S. Pal1, P. Das1, R. Sahu2 and S.R. Dash3*

1Infogain India Pvt. Ltd., Bengaluru, India

2School of Computer Science & Engineering, KIIT University, Bhubaneswar, Odisha, India

3School of Computer Applications, KIIT University, Bhubaneswar, Odisha, India

Abstract

Neuromarketing is the most rising yet undelved technique even though it has shown immense potential. It has many uses and benefits in the commercial sector as supposedly it can tell which product has potential while analyzing your competition and also stop from manufacturing products which might fail in upcoming market trends. It is supposed to fill the gap between survey results and the actual behavior of the customer at the shop.

It has not been researched well in the past due to limitations of cost-effectiveness of an EEG device. But with the promise of cheap, portable and reliable devices like Emotiv Epoc sensors and Neurosky Mindwaves, we are now able to conduct trials and evaluation in a cost-effective, portable and fast manner in comparison to conventional EEG setups. The chapter has analyzed and studied consumers’ choice with regards to various products and found out the pattern of EEG signals using machine learning techniques according to like and dislike.

In this chapter, we have observed and conducted trials on 25 subjects. We recorded all EEG signals using Emotiv Epoc+ Sensor device with 14 channels recording EEG data from 25 volunteers while observing common products on a display. All volunteers are between 18 and 38 years of age. A set of 13 various products were displayed wherein products had 3 different brands which invariably created 42 unique product images. In total 1,050 EEG signals were captured for all the 25 volunteers. Like and dislike are labeled by each participant for the unique image during the experiment to capture the labeled emotions with their corresponding EEG data. Every product was displayed for 4 s. In the data collection, it is instructed to the volunteers to label their honest opinion towards the products. The proposed approaches have shown the feasibility towards the marketing and provide an additional method to the traditional method for forecasting a product’s performance. These machine learning methods with EEG signals may develop strategies, introduce new products, and find out inflation in the business world. We have noticed that Kernel SVM has performed better than other classifiers.

Keywords: Neuromarketing, bagging decision tree, gaussian bayes, kernel SVM, random forest, EEG signals