대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제40권6호
  • /
  • Pages.13-21
  • /
  • 2024
  • /
  • 2733-6239(pISSN)
  • /
  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
권호 표지
DOI QR코드

DOI QR Code

뇌파를 이용한 시각정보 선호도에 관한 정량적 이해

Quantitative Understanding of Visual Information Preference Using EEG

  • 투고 : 2023.11.02
  • 심사 : 2024.05.13
  • 발행 : 2024.06.30
⟨ 이전 논문 다음 논문 ⟩

초록

Visual information preference plays a fundamental role in decision-making, linking it to essential areas of architecture. This research seeks to provide a quantitative understanding of visual preference by integrating logistic regression analysis and brainwave physical data using electroencephalography. The primary objective of this research is to elucidate the neural correlates of visual preference by examining the electroencephalographic (EEG) responses to various visual stimuli. Specifically, this research aims to quantify the differential neural responses associated with the preference for diverse visual content. A series of experiments were conducted to achieve these objectives, in which participants were exposed to a range of visual stimuli while their EEG activity was recorded. The research methods employed advanced signal processing, coding, and surveys to analyze the EEG data, seeking patterns and correlations between brainwave physical data of event-related potentials for visual information preference. This study unveiled that distinct EEG features, including frequency, amplitude, event-related potentials, power spectra, and topographical patterns, are strongly correlated with individual visual preferences. The bivariate correlation analysis indicates that independent variables of the area of positive (P200) to negative (N300) cycles, The intensity or magnitude of the 20-30Hz beta wave spectrum, and the number of positive (P200) cycles are affecting contributors to the dependent variables with a significance level of P < 0.01 and values of .668, .649, and .642, indicating strong positive relationships, respectively. In conclusion, this research contributes to our ability to objectively quantify and interpret visual information preference, harnessing brainwave analysis's power to optimize visual information's impact on architectural preferences.

키워드

과제정보

이 연구는 2022년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호: 2022R1F1A106361811135821106000101

참고문헌

  1. Bulthoff, H. H., & Edelman, S. (1992). Psychophysical support for a two-dimensional view interpolation theory of object recognition. PNAS, 89(1), 60-64. https://doi.org/10.1073/pnas.89.1.60
  2. Cejudo, A. B., Lopez-Rojas, C., Gomez-Ariza, C. J., & Bajo, M. T. (2022). ERP correlates of prospective memory and cue focality in children. Brain Sciences, 12(5), 533.
  3. Golshan, F., Moss, D., Sun, G., Krigolson, O., Cruz, M. T., Loehr, J., & Mickleborough, M. (2022). ERP evidence of heightened attentional response to visual stimuli in migraine headache disorders. Experimental Brain Research, 240(9), 2499-2511.
  4. Hamm, J. P., Johnson, B. W., & Kirk, I. J. (2002). Comparison of the N300 and N400 ERPs to picture stimuli in congruent and incongruent contexts. Clinical Neurophysiology, 113(8), 1339-1350.
  5. Kang, J. H., Kim, S. J., Cho, Y. S., & Kim. S. P. (2015). Modulation of alpha oscillations in the human EEG with facial preference. PLoS ONE, 10(9).
  6. Kim, D. Y., Lee, J. H., Park, M. H., Choi, Y. H., & Park, Y. O. (2017). Trends in brain wave signal and application tehcnology. ETRI.
  7. Kim, S., Park, H., & Choo, S. (2021). Effects of changes to architectural elements on human relaxation-arousal responses: based on VR and EEG. Int. J. Environ. Res. Public Health, 18(8).
  8. Kumar, M., Federmeier, K. D., & Beck, D. M. (2021). The N300: an index for predictive coding of complex visual objects and scenes. Cerebral Cortex Communications, 2(2).
  9. Li, J., Jin, Y., Lu, S., Wu, W., & Wang, P. (2020). Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy and Building, 224.
  10. Maguire, M. J., Magnon, G., Ogiela, D. A., Egbert, R., & Sides, L. (2013). The N300 ERP component reveals developmental changes in object and action identification. Developmental Cognitive Neuroscience, 5, 1-9. https://doi.org/10.1016/j.dcn.2012.11.008
  11. Mozer, M. C. (2001). Object recognition: theories. International Encyclopedia of the Social & Behavioral Sciences, 10781-10785.
  12. Patel, S. H., & Azzam, P. N. (2005). Characterization of N200 and P300: selected studies of the event-related potential. International Journal of Medical Sciences, 2(4), 147-154. https://doi.org/10.7150/ijms.2.147
  13. Portella, C., Machado, S., Arias-Carrion, O., Sack, A. T., Silva, J. G., Orsini, M., & Ribeiro, P. (2012). Relationship between early and late stages of information processing: an event-related potential study. Neurol Int., 4(3), e16.
  14. Proverbio, A. M., Tacchini, M., & Jiang, K. (2023). ERP arkers of visual and auditory imagery. Brain and Cognition, 166.
  15. Sur, S., & Sinha, V. K. (2009). Event-related potential: an overview. Industrial Psychiatry Journal, 18(1), 70-73. https://doi.org/10.4103/0972-6748.57865
  16. Walla P., Chang, M., Schaefer, K., & Windhager, S. (2020). Social perception of faces: brain imaging and subjective ratings. Brain Sciences, 10(11):861.
  17. Youm, S. H., Lee, J. Y., & Choi, Y. R. (2021). A study on stimulation of ceiling height and duration of stay using VR and EEG. Journal of the Architectural Institute of Korea, 37(2).