Labeling ground truth preference (like/dislike) in EEG-based neuromarketing studies can be done in a few different ways. One approach is to use self-reported liking/disliking of the product as the ground truth, as you mentioned. In this case, the EEG data can be analyzed to identify patterns or features that correspond to the self-reported liking/disliking. This can be done through various machine learning algorithms, such as support vector machines (SVMs) or artificial neural networks (ANNs), which can learn to classify the EEG signals based on the ground truth labels.

Another approach to labeling the ground truth is to use implicit measures of preference, such as reaction times or physiological responses (e.g., skin conductance), instead of self-report. This can help avoid potential biases or discrepancies between the self-reported preference and the actual preference.

Regarding the second question on the real-world application of EEG-based neuromarketing, it is important to note that these studies are typically conducted in a controlled laboratory setting with a small sample size. Therefore, it may be difficult to generalize the results to real-world scenarios with larger and more diverse populations. However, EEG-based neuromarketing can provide valuable insights into the neural mechanisms underlying consumer preferences, which can inform marketing strategies and product design.

For example, EEG-based neuromarketing can be used to optimize product features or marketing messages to better appeal to consumers. Additionally, it can be used to evaluate the effectiveness of marketing campaigns or to identify target audiences for specific products or services. However, it is important to consider ethical and privacy concerns when implementing neuromarketing techniques in the real world.

Aparna Sathya Murthy , Alireza Falakdin : Thank you so much for your answers.