  Neuromarketing is an emerging field that combines neuroscience and marketing
to understand the factors that influence consumer decisions better. The study
proposes a method to understand consumers' positive and negative reactions to
advertisements (ads) and products by analysing electroencephalogram (EEG)
signals. These signals are recorded using a low-cost single electrode headset
from volunteers belonging to the ages 18-22. A detailed subject dependent (SD)
and subject independent (SI) analysis was performed employing machine learning
methods like Naive Bayes (NB), Support Vector Machine (SVM), k-nearest
neighbour and Decision Tree and the proposed deep learning (DL) model. SVM and
NB yielded an accuracy (Acc.) of 0.63 for the SD analysis. In SI analysis, SVM
performed better for the advertisement, product and gender-based analysis.
Furthermore, the performance of the DL model was on par with that of SVM,
especially, in product and ads-based analysis.
