Stralen Pratasik, Adhi Dharma Wibawa, Diah Puspito Wulandari, Yuri Pamungkas, Natan Derek
In the rapidly evolving field of neuromarketing, understanding the neural basis of consumer decision-making is crucial. Identifying neural signals associated with buying intention can provide valuable insights into consumer behaviour, enhancing the effectiveness of marketing strategies. This study investigates buying intention by analyzing EEG signals recorded from 28 participants while they viewed video advertisements. The EEG data were acquired using the OpenBCI system, with electrodes placed at Fp1, Fp2, F7, F8, O1, and O2. The data was preprocessed to remove artifacts, segmented into, and decomposed into alpha, beta, and gamma frequency bands. Time-domain features (mean, mean absolute value, standard deviation, and Hjorth parameters) and frequency-domain features (power spectral density) were extracted. The total number of features obtained was 126 features. In order to reduce redundancy and to avoid overfitting in machine learning algorithm, feature selection was applied using the Pearson correlation approach. The result showed that distinct neural activity patterns were observed between high and low buying intention states, with the gamma band showing the most pronounced differences. Specifically, high buying intention conditions exhibited dominant activity across both frontal and occipital regions. Furthermore, classification analysis using machine learning, with feature selection, showed that the Random Forest algorithm achieved the highest accuracy of 98.7% with 58 features. Despite the limitations of a small sample size and a sparse 6-electrode configuration, the model achieved 94.4% accuracy using just six optimally selected features. These findings not only highlight the significance of the gamma band in understanding the buying intention states but also suggest practical implications for neuromarketing applications. © 2025, Department of Agribusiness, Universitas Muhammadiyah Yogyakarta. All rights reserved.
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Department of Medical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Department of Informatics Engineering, Manado State University, Indonesia; Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Norway