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Neuromarketing is a blooming interdisciplinary field that tries to understand the biology of consumer behavior by combining neuroscience with marketing. This technique can be used to grasp consumers’ hidden choices, intentions and decisions by analyzing their physiological and brain signals. Electroencephalography (EEG) is one of the popular neuroimaging techniques to capture and record the neural activity of the brain. Numerous research projections have been made in this field to achieve better results. Earlier approaches did not prioritize effective EEG signal preprocessing and classification methods. This paper presents a model to recognize consumer preferences by analyzing and classifying EEG signals. In this model, EEG signals are decomposed into many subbands using wavelet transform. An enhanced wavelet thresholding method is proposed to eliminate noise from subbands. Several wavelet features are computed from each subband and then fed as input to classifiers. Finally, three different machine learning classifiers are used to classify the signal between like and dislike. The classifiers are K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). EEG signals from 25 people are collected to verify the developed system’s performance. The effectiveness of the developed method with different classifiers is validated by varying brain lobe features and band features. In comparison to other classifiers like KNN and MLP, the designed system with the SVM classifier performs better and achieves an accuracy of 98.21%. The experimental findings for the developed system suggest that research in this area has the potential to alter and enhance marketing tactics for the benefit of both manufacturers and consumers, ultimately leading to a mutually beneficial outcome.

With the South Asian economy swiftly integrating into global markets, the restaurant industry in India and Bangladesh has thrived, providing a wide range of global cuisines. Along with other foreign restaurants, Japanese restaurants have gained traction in recent years. This paper seeks to delve into the Japanese culinary industry in South Asia, aiming to comprehend the factors influencing consumer choice and formulate an effective market development strategy. Employing the theory of planned behavior (TPB), the study explores the determinants of Indian and Bangladeshi consumers’ intentions to choose Japanese restaurants. Utilizing qualitative research methods, 36 in-depth interviews (IDIs) were conducted, with 20 Indian and 16 Bangladeshi respondents. A semi-structured questionnaire covering demographics, attitudes, subjective norms (SN), perceived behavioral control and other factors including price concern, food authenticity and religion was employed. Analysis of interview transcripts using NVivo 10 software revealed that participants generally had an average economic condition. Notably, religion emerged as a significant factor influencing purchase intentions in Bangladesh, while attitude and perceived behavioral control are the most significant contributors to Indian consumers’ perceptions. Additionally, the study found that both Indian and Bangladeshi consumers’ perceptions were significantly influenced by the authenticity of Japanese cuisine and food prices positively and negatively. The findings offer valuable insights into the Japanese culinary industry promotion in India and Bangladesh.