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The Role of Gamma Band in Buying Intention Prediction Using Random Forest and Pearson Correlation for Feature Selection Based on EEG Signal

Journal of Robotics and Control (JRC) · Vol. 6 No. 5 (2025) · hlm. 2522–2534

DOI
10.18196/jrc.v6i5.26135
ISSN
2715-5072, 2715-5056
Terbit
2025-10-29
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Institut Teknologi Sepuluh Nopember
  • Rajamangala University of Technology
  • Hallym University
  • Universiti of Malaysia Sabah
  • Guizhou University of Engineering Science
  • Norwegian University of Science and Technology
  • University of Newcastle Australia
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Rekan penulisOrganisasiArtikel bersama
Made KrisnandaUniversity of Newcastle Australia4
Yamin ThweRajamangala University of Technology3
Myo Min AungRajamangala University of Technology3
Padma Nyoman CrisnapatiRajamangala University of Technology3
M. N. A. UdaUniversiti of Malaysia Sabah2
U. HashimUniversiti of Malaysia Sabah2
Yuri PamungkasInstitut Teknologi Sepuluh Nopember2
Riva Satya RadiansyahInstitut Teknologi Sepuluh Nopember2
Santi Wulan PurnamiInstitut Teknologi Sepuluh Nopember1
Arief KurniawanInstitut Teknologi Sepuluh Nopember1
Alvin NathanaelInstitut Teknologi Sepuluh Nopember1
Anda L. JunianiPoliteknik Perkapalan Negeri Surabaya1
Wardah Rahmatul IslamiyahUniversitas Airlangga1
Abdul KarimHallym University1
Adhi Dharma WibawaInstitut Teknologi Sepuluh Nopember1
Siti Dwi SuryaniInstitut Teknologi Sepuluh Nopember1
Tegar Anugrah FirdausInstitut Teknologi Sepuluh Nopember1
Bryan RaisInstitut Teknologi Sepuluh Nopember1
Marcelinus Jonathan SalimInstitut Teknologi Sepuluh Nopember1
Andhika Rastra NegaraInstitut Teknologi Sepuluh Nopember1
Muhammad Qaiser Zaydan ZiaInstitut Teknologi Sepuluh Nopember1
Gao YulanGuizhou University of Engineering Science1
Abdul KarimHallym University1
Yulan GaoGuizhou University of Engineering Science1
Abdul KarimHallym University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">The Role of Gamma Band in Buying Intention Prediction Using Random Forest and Pearson Correlation for Feature Selection Based on EEG Signal</dc:title>
	<dc:creator>Pratasik, Stralen</dc:creator>
	<dc:creator>Wibawa, Adhi Dharma</dc:creator>
	<dc:creator>Wulandari, Diah Puspito</dc:creator>
	<dc:creator>Pamungkas, Yuri</dc:creator>
	<dc:creator>Derek, Natan</dc:creator>
	<dc:subject xml:lang="en-US">Buying Intention Prediction</dc:subject>
	<dc:subject xml:lang="en-US">Gamma Band</dc:subject>
	<dc:subject xml:lang="en-US">Random Forest Classification</dc:subject>
	<dc:subject xml:lang="en-US">Pearson Correlation</dc:subject>
	<dc:subject xml:lang="en-US">EEG Heatmap Visualization</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-10-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/26135</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i5.26135</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 5 (2025); 2522-2534</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/26135/11976</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Stralen Pratasik, Adhi Dharma Wibawa, Diah Puspito Wulandari, Yuri Pamungkas, Natan Derek</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>