PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

• Abujelala, M., Sharma, A., Abellanoza, C., & Makedon, F. (2016). Brain-EE: Brain enjoyment evaluation using commercial EEG headband. In Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments (pp. 1-5). ACM Publishing. https://doi.org/10.1145/2910674.2910691

• Barnett, S. B., & Cerf, M. (2017). A ticket for your thoughts: Method for predicting content recall and sales using neural similarity of moviegoers. Journal of Consumer Research, 44(1), 160-181. https://doi.org/10.1093/jcr/ucw083

• Beres, A. M. (2017). Time is of the essence: A review of electroencephalography (EEG) and event-related brain potentials (ERPs) in language research. Applied Psychophysiology Biofeedback, 42(4), 247-255. https://doi.org/10.1007/s10484-017-9371-3

• Bhagchandani, A., Bhatt, D., & Chopade, M. (2018). Various big data techniques to process and analyse neuroscience data. In 2018 5th International Conference on “Computing for Sustainable Global Development (pp. 397-402). INDIACom.

• Boksem, M. A. S., & Smidts, A. (2015). Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. Journal of Marketing Research, 52(4), 482-492. https://doi.org/10.1509/jmr.13.0572

• Casson, A. J., Abdulaal, M., Dulabh, M., Kohli, S., Krachunov, S., & Trimble, E. (2018). Electroencephalogram. In T. Tamura & W. Chen (Eds), Seamless healthcare monitoring (pp. 45-81). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-69362-0_2

• Coenen, A., & Zayachkivska, O. (2013). Adolf Beck: A pioneer in electroencephalography in between Richard Caton and Hans Berger. Advances in Cognitive Psychology, 9(4), 216-221. https://doi.org/10.5709/acp-0148-3

• Deolindo, C. S., Ribeiro, M. W., Aratanha, M. A., Afonso, R. F., Irrmischer, M., & Kozasa, E. H. (2020). A critical analysis on characterising the meditation experience through the electroencephalogram. Frontiers in Systems Neuroscience, 14(August), 1-29. https://doi.org/10.3389/fnsys.2020.00053

• Doma, O. O. (2019). EEG as input for virtual reality. In N. Lee (Ed.), Encyclopedia of computer graphics and games (pp. 1-4). Springer International Publishing. https://doi.org/10.1007/978-3-319-08234-9_176-1

• Hill, H. (2019). Exploring the limitations of event-related potential measures in moving subjects. Case studies of four different technical modifications in ergometer rowing. BioRxiv, 31, 1-23. https://doi.org/10.1101/578534

• House, P. M., Pelzl, S., Furrer, S., Lanz, M., Simova, O., Voges, B., Stodieck, S. R. G., & Brückner, K. E. (2020). Use the mixed reality tool “VSI Patient Education” for more comprehensible and imaginable patient education before epilepsy surgery and stereotactic implantation of DBS or stereo-EEG electrodes. Epilepsy Research, 159(October 2019), Article 106247. https://doi.org/10.1016/j.eplepsyres.2019.106247

• Husain, A. M., & Sinha, S. R. (2020). Continuous EEG monitoring: Principles and practice. Journal of Clinical Neurophysiology, 37(3), 274-274. https://doi.org/10.1097/wnp.0000000000000571

• Ibrahim, S. A. S., Hamzah, N., Wahab, A. R. A., Abdullah, J. M., Malim, N. H. A. H., Sumari, P., Idris, Z., Mokhtar, A. M., Ghani, A. R. I., Halim, S. A., & Razak, S. A. (2020). Big brain data initiative in universiti sains malaysia: Challenges in brain mapping for Malaysia. Malaysian Journal of Medical Sciences, 27(4), 1-8. https://doi.org/10.21315/mjms2020.27.4.1

• Kaplan, R. M. (2011). The mind reader: The forgotten life of Hans Berger, discoverer of the EEG. Australasian Psychiatry, 19(2), 168-169. https://doi.org/10.3109/10398562.2011.561495

• Koudelková, Z., & Strmiska, M. (2018). Introduction to the identification of brain waves based on their frequency. MATEC Web of Conferences, 210, 1-4. https://doi.org/10.1051/matecconf/201821005012

• Lau-Zhu, A., Lau, M. P. H., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36(October 2018), Article 100635. https://doi.org/10.1016/j.dcn.2019.100635

• Lin, M. H. J., Cross, S. N. N., Jones, W. J., & Childers, T. L. (2018). Applying EEG in consumer neuroscience. European Journal of Marketing, 52(1-2), 66-91. https://doi.org/10.1108/EJM-12-2016-0805

• Liu, X., Zhang, J., Hou, G., & Wang, Z. (2018). Virtual reality and its application in military. IOP Conference Series: Earth and Environmental Science, 170(3), Article 032155. https://doi.org/10.1088/1755-1315/170/3/032155

• Maddirala, A. K., & Shaik, R. A. (2018). Separation of sources from single-channel EEG signals using independent component analysis. IEEE Transactions on Instrumentation and Measurement, 67(2), 382-393. https://doi.org/10.1109/TIM.2017.2775358

• Maples-Keller, J. L., Bunnell, B. E., Kim, S. J., & Rothbaum, B. O. (2017). The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. Harvard Review of Psychiatry, 25(3), 103-113. https://doi.org/ 10.1097/HRP.0000000000000138

• McIntosh, J., Rodgers, M., Marques, B., & Gibbard, A. (2019). The use of VR for creating therapeutic environments for the health and well-being of military personnel, their families and their communities. Journal of Digital Landscape Architecture, 2019(4), 185-194. https://doi.org/10.14627/537663020

• Mosslah, A. A., Mahdi, R. H., & Al-Barzinji, S. M. (2019). Brain-computer interface for biometric authentication by recording signal. Computer Science & Information Technology, 2019, 153-162. https://doi.org/10.5121/csit.2019.90613

• Plassmann, H., Venkatraman, V., Huettel, S., & Yoon, C. (2015). Consumer neuroscience: Applications, challenges, and possible solutions. Journal of Marketing Research, 52(4), 427-435. https://doi.org/10.1509/jmr.14.0048

• Read, G. L., & Innis, I. J. (2017). Electroencephalography (EEG). In J. Matthes (Ed.), The international encyclopedia of communication research methods (pp. 1-18). John Wiley & Sons, Inc. https://doi.org/10.1002/9781118901731.iecrm0080

• Reyes, L. M. S., Reséndiz, J. R., & Ramírez, G. N. A. (2019). Trends of clinical EEG systems: A review. In 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings (pp. 571-576). IEEE Publishing. https://doi.org/10.1109/IECBES.2018.8626680

• Rojas, G. M., Alvarez, C., Montoya, C. E., de la Iglesia-Vayá, M., Cisternas, J. E., & Gálvez, M. (2018). Study of resting-state functional connectivity networks using EEG electrodes position as seed. Frontiers in Neuroscience, 12(APR), 1-12. https://doi.org/10.3389/fnins.2018.00235

• Seal, A., Reddy, P. P. N., Chaithanya, P., Meghana, A., Jahnavi, K., Krejcar, O., Hudak, R., & Jiang, Y. Z. (2020). An EEG database and its initial benchmark emotion classification performance. Computational and Mathematical Methods in Medicine, 2020¸ Article 8303465. https://doi.org/10.1155/2020/8303465

• Siuly, S., Li, Y., & Zhang, Y. (2016). Significance of EEG signals in medical and health research. In EEG signal analysis and classification (pp. 23-41). Springer. https://doi.org/https://doi.org/10.1007/978-3-319-47653-7_2

• Suhaimi, N. S., Mountstephens, J., & Teo, J. (2020). EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities. Computational Intelligence and Neuroscience, 2020, Article 8875426. https://doi.org/10.1155/2020/8875426

• Tudor, M., Tudor, L., & Tudor, K. I. (2005). The history of electroencephalography. Acta Medica Croatica, 59(4), 307-313.

• Vaid, S., Singh, P., & Kaur, C. (2015). EEG signal analysis for BCI interface: A review. In International Conference on Advanced Computing and Communication Technologies, ACCT ( pp. 143-147). IEEE Publishing. https://doi.org/10.1109/ACCT.2015.72

• Xue, G., Chen, C., Lu, Z. L., & Dong, Q. (2010). Brain imaging techniques and their applications in decision-making research. Acta Psychologica Sinica, 42(1), 120-137. https://doi.org/10.3724/sp.j.1041.2010.00120

• Yu, J. H., & Sim, K. B. (2016). Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik, 127(20), 9711-9718. https://doi.org/10.1016/j.ijleo.2016.07.074