| Title |
Quantitative Understanding of Visual Information Preference Using EEG |
| Authors |
이상현(Lee, Sanghyun) ; 이지환(Lee, Jeehwan) |
| DOI |
https://doi.org/10.5659/JAIK.2024.40.6.13 |
| Keywords |
Neural Mechanism; Electroencephalography; Brain Wave; Event-Related Potentials; Visual Information Preference |
| Abstract |
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. |