Abstract:
Neuromarketing is an emerging brain-computer interface (BCI) research field that aims to understand consumers' internal decision-making processes when choosing which products to buy. It provides valuable insights for marketers to improve their marketing strategies based on consumers' impressions. However, the current status of Electroencephalography (EEG)-based preference prediction and its classification accuracy is still below optimal. The performance of EEG-based preference detection systems depends on a suitable pre-processing pipeline selection and proper data labelling since noisy EEG data and wrong labeling are likely not to give better results. Most of the previous studies followed a traditional EEG pre-processing pipeline, and also recently, an advanced automated EEG pre-processing pipeline has been introduced in the literature. In this study, I have proposed an optimal EEG pre-processing pipeline and compared it with the other two existing methods. I collected raw EEG data and pre-processed them using the three pre-processing pipelines. I then extracted many statistical and frequency domain features and employed several machine learning (ML) classification models. After implementing my proposed pipeline to pre-process EEG data, I observed a significant improvement in classification accuracies compared to the other two existing methods. Another essential thing is to label the data properly so that ML models can be trained accurately. Previous studies have used different ML models to predict subjects' future preferences based on subjective labeling, i.e., the preferences are self-reported by the subjects. However, such labeling methods contradict the main goal of Neuromarketing research, which is to detect genuine preferences from brain data rather than rely on subjects-reported data. This leads to the necessity of an objective labeling approach where the labels can be detected directly from the brain data. In this study, I proposed an objective labeling method for EEG-based preference prediction in Neuromarketing. I compared the performance of different ML models trained on objective and subjective labeling using two datasets: a publicly available Neuromarketing dataset and a new dataset created in my experiments. My findings demonstrate that the ML models trained on objective labeling provided better classification results than the ones trained on subjective labeling. The result aligns with the goals of Neuromarketing research, as it offers an automated way of labeling data and opens up new avenues for future research. Also, among the prominent components of promotions, language has a major impact on consumers' minds. I investigated the effects of foreign languages (FL) on consumers' preferences in Neuromarketing and found that consumers show risk adoption tendencies when exposed to product ads in FL instead of their native languages (NL).
