EEG-Based Preference Classification for Neuromarketing Application

Neuromarketing is a modern marketing research technique whereby consumers’ behavior analyzed using neuroscientific approaches. In this work, an EEG database of responses to image advertisements was created, processed, and studied with the goal building predictive models that can classify preference based on their data. Several types analysis were performed three classifier algorithms, namely, SVM, KNN, NN pattern recognition. The maximum accuracy sensitivity values are reported be 75.7% 95.8%, respectively, for female subjects KNN classifier. addition, frontal region electrodes yielded best selective channel performance. Finally, conforming obtained results, deemed classification problems. newly created dataset results derived from it will help communities conduct further studies in neuromarketing.