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In Figure 3.9, we shall be doing a comparative analysis of each user’s data’s performance regarding each individual algorithm in a chart form.

In this study, we applied our knowledge of EEG data and Machine Learning to cohabit in a system for correctly analyze and predict the consumer’s choice when surveying different brands of same type of products. We had 25 males perform this initial study and it resulted in a viable feasibility for developing solutions using EEG data to enhance productivity, cut down on losses and shifting the paradigm of marketing to new heights. We have noticed that on a user-level Kernel SVM has performed better than others in majority of the cases for identifying like/dislike. It has also recorded the highest accuracy in Master file run of 56.2% among others. We have observed that Kernel SVM: Sigmoid is significant to our study and we shall try different kernels in this form to test better results.

In the following Figures 3.10 and 3.11 we shall be showing you the Approximate Brain’s EEG activity map we have derived for like/dislike states of mind in our Neuromarketing study.

Figure 3.9 Result of 25-users compared with different algorithms.