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
3.1 Introduction
Brain is perhaps the most significant organ of our body which consists of billions of neurons which in turn use electric pulses for communicating among themselves. There is immense amount of electrical activity in our brain because of transmission of neuro signals in the form of electric pulses. These neuro signals are what is collectively known as brainwaves. They consist of synchronized and sequential electric pulses from neurons to various parts of our body via nerves. These brainwaves can be analyzed and detected by an equipment called Electroencephalograph. Traditionally the machines used to detect EEG signals where very large and costly but nowadays they have been miniaturized into EEG biosensors. EEG is a significant part of signal analysis because its amplitude graphs depict the various stimulations as well as brain states.
3.1.1 Why BCI
The objective of our study is empowering the users to explore the different avenues that come under the field of Brain Computer Interfaces (BCI), implementing user-friendly and economical equipment which have been recently been made viable for common public use. The domain of BCI is the influential force behind the goal of full utilization of Electroencephalography (EEG). We use electrodes on the scalp of users to record the brain activity. In the past, we have focused highly on creating solutions from a medical context, helping extreme cases of paralysis or disablement in regard to motor functions by mapping EEG signals to the respective cognitive actions.
But now BCI solutions are no longer limited to just patients for treatment, with the change in focus of general public towards living a healthier lifestyle with the aid of modern technology, young people are a prime target group who are willing to adapt to EEG devices as a dimension giving them timely updates about their mental ability and wellness. It starts with treatment and can go till far as entertainment as more and more companies start working on Mind-Controlled gaming technology. This exciting phase makes EEG to be more available, cost effective and firms become more willing to invest into this technology for general purpose uses.
One of the prime manufacturers of cost-effective EEG headsets is Emotiv with a slogan “Neurotech for the Global Community”. They have an EEG device named Emotiv EPOC+ sensor device which is a low-cost and portable headset aimed for the consumer market. Historically EEG machines were reserved for rich people but now it can be used by anyone in the shape of a user, developer or analyst. This helps us to accelerate the growth and research done in the field of BCI. The strategy of our study is to utilize this piece of technology and create viable results in Neuromarketing.
3.1.2 Human–Computer Interfaces
Since past few years, major advances have been done in the approach for interaction of users with machines. Standard physical interactive devices for computers were keyboard and mouse, but now more user-centric devices are being developed to deeply incorporate them into the lifestyle of users.
Most of the previous devices required an action to be performed for interacting with the machine. EEG counters this point by controlling the actions via brains stimuli functions. This in turn becomes life-saving for people with limited motor skills due to diseases and disabilities like paralyzed patients. This solution is also known as Mind–Machine Interface or “Brain Machine Interface” which is a direct path from brain to an external device.
3.1.3 What is EEG
An Electroencephalogram is basically a reading of brain’s electric voltage fluctuations as read on scalp electrodes. It is the approximate cumulative electrical activity of neurons. This process is one of the best non-penetrative interfaces because of its temporal resolution. But it has hindrances like susceptibility to noise which is a very prominent barrier to implementing EEG devices as BCI solutions. It requires extensive training for users and models to provide substantial results in a consistent manner.
As for example, Niels Birbaumer from University of Tubingen had brought paralyzed patients in mid-1990s for training them to control the slow cortical potentials to be utilized in order for them to control a computer’s cursor by binary signaling. They were slow, as in they required 1 h to write 100 characters and training them took several months but it appeared as a breakthrough possibility.
3.1.4 History of EEG
Hans Berger was the man who discovered that there is significant electrical activity in the brain and developed the initial process known as Electroencephalography today. In the year 1924 he captured the first brain signal and by analyzing them he found oscillatory activity in the form of alpha wave (8–12 Hz) called Berger’s wave. During early days he inserted silver wires in the scalp of patients then after graduating to using silver foils which in turn were connected to a “Lippmann Capillary Electrometer”. Later on he experimented with galvanometers and after significant analysis he started creating brain maps of electrical pulses for specific brain diseases. This all led to the discovery of EEG and created new possibilities in human advancement.
3.1.5 About Neuromarketing
Generally marketing procedures or tools include surveying, interviews, target groups, etc. where participants willingly give feedback for their thoughts and opinions on a product. These procedures have a drawback of not considering the unconscious decision-making characteristics. EEG has the potential to identify these emotions and influence the decision-making process. It is found that 90% of the decision making consists of factors from the subconscious mind. In this domain, the analysis of the subconscious mind presents the true choices of users more accurately than other methods. It has the potential to factor in characteristics about decision making in users which cannot be accurately pin-pointed in other methods. Neuromarketing fills the gap between the results of traditional marketing methods and real decisions of consumers.
In order to analyze the consumer’s behavior we factor in his sensorimotor and mental feedback with the combination of eye-tracking, skin conductance, galvanic skin response and facial electromyography which all in turn contribute to create a sequential flow chart of a consumer’s response and various stimuli which resulted in the failure or successful of purchase of the product.
This technology came around prominence in 2002 with the initiatives of Brighthouse and SalesBrain who developed marketing solutions
