<p>Evaluation of marketing stimuli such as static advertisements, video advertisements, promotions, etc. is an important part of marketing research. Traditionally, the evaluation is done through large surveys, focus group discussions, questioner-based surveys, etc. However, these methods are time-consuming and expensive. Here, neuromarketing offers a solution that can improve marketing research. Recently, several EEG-based neuromarketing studies proved the potential of neuromarketing as a marketing research tool. However, most of these researchers used research-grade EEG devices. Research-grade EEG devices are bulky and require high setup time. This is why, despite much successful research, the technology is yet to be implemented on a day-to-day basis. On the other hand, consumer-grade BCI devices are cheap, wearable, and offer little to no setup time. So, it is ideal for day-to-day implementation. However, consumer-grade BCI lacks in terms of signal quality and available channel number, and it has not been widely tested in neuromarketing. So, in this study, we compared a research-grade EEG device and a consumer-grade BCI device for consumers’ preference prediction using a machine learning framework. We designed an experiment to record participants' EEG data while they were shown marketing stimuli. Their response has been taken as affective attitude (AA) purchase intention (PI). After that, binary classification was performed. We achieved 80.71% and 82.07% accuracy for predicting purchase intention and affective attitude using the research-grade EEG device, whereas it was 78.31% and 81.23% using the consumer-grade BCI device. These findings prove that consumer-grade BCI can be a viable option for day-to-day usage in neuromarketing.</p>