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Taghaddossi, mahdi, Moradi, Poorya, Moradi, Mohammad Hasan. (1401). Brain Effective Connectivity Comparison in the Four States of Confrontation to the Brands During Shopping. نشریه مهندسی عمران امیرکبیر, 54(2), 185-198. doi: 10.22060/eej.2022.21097.5457
mahdi Taghaddossi; Poorya Moradi; Mohammad Hasan Moradi. "Brain Effective Connectivity Comparison in the Four States of Confrontation to the Brands During Shopping". نشریه مهندسی عمران امیرکبیر, 54, 2, 1401, 185-198. doi: 10.22060/eej.2022.21097.5457
Taghaddossi, mahdi, Moradi, Poorya, Moradi, Mohammad Hasan. (1401). 'Brain Effective Connectivity Comparison in the Four States of Confrontation to the Brands During Shopping', نشریه مهندسی عمران امیرکبیر, 54(2), pp. 185-198. doi: 10.22060/eej.2022.21097.5457
Taghaddossi, mahdi, Moradi, Poorya, Moradi, Mohammad Hasan. Brain Effective Connectivity Comparison in the Four States of Confrontation to the Brands During Shopping. نشریه مهندسی عمران امیرکبیر, 1401; 54(2): 185-198. doi: 10.22060/eej.2022.21097.5457

Brain Effective Connectivity Comparison in the Four States of Confrontation to the Brands During Shopping

AUT Journal of Electrical Engineering
مقاله 3، دوره 54، شماره 2، اسفند 2022، صفحه 185-198    اصل مقاله (1.42 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2022.21097.5457
نویسندگان
mahdi Taghaddossi1؛ Poorya Moradi2؛ Mohammad Hasan Moradi* 1
1Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
2Industrial Engineering, Engineering Faculty, Khatam University, Tehran, Iran
چکیده
Neuromarketing assists us to uncover the subconscious effects of marketing stimuli on consumers' brains from a neuroscientific perspective. One of the important effects of brands on human brain is in the level of directed relations between brain areas which were less considered in neuromarketing studies. In this paper, we used the EEG signals recorded during the confrontation of participants to the brands in the virtual shopping center. 20 participants (10 females and 10 men) were contributed to the experiment. After preprocessing, extracted brain sources were clustered to brain areas. Effective connectivity between brain areas was calculated using the Generalized Partial Directed Coherence (GPDC) index in four different states of watching brands (1. unfamiliar and undesired brands 2. familiar and undesired brands 3. unfamiliar and desired brand 4. desired and familiar brands). Statistical analysis of these states in watching familiar brands showed that almost all of the brain areas have stronger relation to each other. Watching unfamiliar brands between hemispheric relations showed that they are stronger when brands are desired, and interhemispheric relations are stronger when brands are not desired. Additionally, in watching familiar brands, left-brain relations are stronger when the brands are desired and right-brain relations are stronger when the brands are undesired. As the brands were shown for 2 seconds, the connectivity values in 1st second and 2nd second of watching brands do not have significant differences. Additionally, connectivity values are stronger in lower frequency bands of the brain during watching the brands in the shopping center. 
کلیدواژه‌ها
Neuromarketing؛ EEG signals؛ K-mean clustering؛ Brain directed relations؛ Statistical analysis
مراجع
  1. A. Dash, R. Dash, and B. Das, “Neuro Marketing-An Expanding Field Of Marketing Research,” International Journal of Research in Engineering and Science (IJRES), vol.5, no.3, pp.67-70, 2017.
  2.  M. Nilashi et al., “Neuromarketing: A Review of Research and Implications for Marketing,” J. Soft Comput. Decision Support Systems, vol. 7, no. 2, pp. 23–31, 2020.
  3.  A. Bazzani et al., “Is EEG Suitable for Marketing Research? A Systematic Review,” Frontiers in Neurosciece, vol. 14, no. 594566, 2020.
  4.  G. Vecchiato et al., “The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: An application in a neuromarketing experiment,” Journal of Neuroscience Methods, vol. 191, no. 2, pp. 283–289, 2010.
  5. M. Balconi, B. Stumpo, and F. Leanza, “Advertising, brand and neuromarketing or how consumer brain works,” Neuropsychological Trends, vol. 16, no. 1, pp. 15–21, 2014.
  6. G. Vecchiato et al., “Neurophysiological tools to investigate consumer’s gender differences during the observation of TV commercials,” Computational and Mathematical Methods in Medicine, vol. 2014, 2014.
  7. N. Goto et al., “Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task,” Biological Psychology, vol. 128, pp. 11–20, 2017.
  8. G. Ural, F. Kaçar, and S. Canan, “Wavelet Phase Coherence Estimation of EEG Signals for Neuromarketing Studies,” NeuroQuantology, vol. 17, no. 02, pp. 112–120, 2019.
  9.  M. J. Darvishi, A. M. Nasrabadi, and T. Curran, “Effective connectivity measuring of ERP signals in recognition memory process by Generalized Partial Directed Coherence,” 23rd Iranian Conference on Biomedical Engineering. 2016.
  10.  L. Astolfi et al., “Neural basis for brain responses to TV commercials: A high-resolution EEG study,” 522 IEEE Transactions on neural systems and rehabilitation engineering., vol. 16, no. 6, pp. 522–531, 2008.
  11. B. Hu, et al., “Effective brain network analysis with resting-state EEG data: A comparison between heroin abstinent and non-addicted subjects,” Journal of neural engineering., vol. 14, 2017
  12.  L. A. Baccala, K. Sameshima, and D. Y. Takahashi, “Generalized Partial Directed Coherence,” Digital Signal Processing., pp. 163–166, 2007.
  13.  M. A. Mosavi and M. H. Moradi, “ERP Signal Analysis in NeuroMarketing Field: Analyzing Effects of Background Advertisement of TV Shows during a Virtual Shopping Task,” MSC thesis, Amirkabir university of Thechnology, Tehran, Iran, 2018.
  14.  Z. Izadi, “EEG Signal Analysis in NeuroMarketing Field: Analyzing Effects of Background Advertisement of TV Shows during a Virtual Shopping Task,” MSC thesis, Amirkabir University of Technology, Tehran, Iran, 2018.
  15.  A A. Delorme and S. Makeig, “EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004.
  16.  L. J. Gabard-Durnam, A. S. M. Leal, C. L. Wilkinson, and A. R. Levin, “The harvard automated processing pipeline for Electroencephalography (HAPPE): Standardized processing software for developmental and high-artifact data,” Frontiers in neuroscienece, vol. 12, pp. 1–24, 2018.
  17.  M. A. Mosavi, M. H. Moradi, and A. Younesi, “Does Oculus Rift Virtual Reality Headset change Precision in A 64-channel EEG Recording?,” first sharif neuroscience symposium, 2019.
  18. M. Chaumon, D. V. M. Bishop, and N. A. Busch, “A practical guide to the selection of independent components of the electroencephalogram for artifact correction,” Journal of Neuroscience Methods, vol. 250, pp. 47–63, 2015.
  19. A. Delorme, et al., “Independent EEG sources are dipolar,” PLoS One, vol. 7, no. 2, 2012.
  20. C. Brunner et al., “Volume conduction influences scalp-based connectivity estimates,” Frontiers in computational neuroscience, vol. 10, no. NOV, pp. 1–4, 2016.
  21. M. Rahimi, M. H. Moradi, and F. Ghassemi, “Comparison of brain effective connectivity in different states of attention and consciousness based on EEG signals,” Biomedical Signal Processing and Control, vol. 51, pp. 393–400, 2019.
  22. T. Mullen, “Source Information Flow Toolbox ( SIFT ) Theoretical Handbook and User Manual,” Theoretical Handbook and User Manual,  Swartz Center for Computational Neuroscience,
     pp. 1–69, 2010.
  23.  A. Ghasemi and S. Zahediasl, “Normality tests for statistical analysis: A guide for non-statisticians,” International Journal of Endocrinology and Metabolism, vol. 10, no. 2,
    pp. 486–489, 2012.
  24.  T. Notes, “How to select appropriate statistical test?,” Journal of Pharmaceutical Negative Results, vol. 1, no. 2, 2010.
  25. D. G. Pereira, A. Afonso, and F. M. Medeiros, “Overview of Friedmans Test and Post-hoc Analysis,” Communications in Statistics - Simulation and Computation, vol. 44, no. 10, pp. 2636–2653, 2015.
  26. J. Pallant, "SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS for Windows", book, McGraw-Hill Education, 3rd eddition. page 223 to 225, 2007.
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