Abstract:
EEG for Neuromarketing is a research project with the goal of making neuromarketing as effective and as affordable for businesses as possible. We used EEG data to understand product preference for people. We used alpha, beta and gamma frontal asymmetries and alpha, beta and gamma band powers to show the role they play in predicting product preference. EEG data from the frontal lobe (electrodes: F3 and F4, FC5 and FC6, F7 and F8, FC1 and FC2, FP1 and FP2) was used for calculating the frontal asymmetries. Data was collected from 13 participants. After preprocessing, SVM and logistic regression were applied on the frontal asymmetries calculated from the EEG data. On all subjects’ data, SVM classifier with linear kernel had the best accuracy of 100%, whereas logistic regression gave the best accuracy of 58.6%.
