TY - JOUR
T1 - The function of color and structure based on eeg features in landscape recognition
AU - Wang, Yuting
AU - Wang, Shujian
AU - Xu, Ming
N1 - Funding Information:
Funding: This research was funded by the National Key R&D Program of China Climate Change Impact and Adaptation in Major Countries along the Belt and Road (2018YFA0606500).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered color, and color images with a filtered structure. The results show that the SVM classifier was the most suitable classifier for landscape classification based on EEG features. The classification accuracy of the landscape picture recognition was up to 98.3% when using beta waves, while the accuracy of the color recognition was 97.5%, and that of the structure recognition was 93.9% when using gamma waves. Secondly, color and structure played a major role in determining the alpha and gamma wave responses, respectively, for all the landscape types, including forest, desert, and water. Furthermore, structure only played a decisive role in forest, while color played a major role in desert and water when using beta waves. Lastly, statistically significant differences between landscape groups and scenario groups with regard to alpha, beta, and gamma rhythms in brain waves were confirmed. The reasonable usage and layout of structure and color will have a very important guiding value for landscape aesthetics in future landscape design and landscape planning.
AB - Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered color, and color images with a filtered structure. The results show that the SVM classifier was the most suitable classifier for landscape classification based on EEG features. The classification accuracy of the landscape picture recognition was up to 98.3% when using beta waves, while the accuracy of the color recognition was 97.5%, and that of the structure recognition was 93.9% when using gamma waves. Secondly, color and structure played a major role in determining the alpha and gamma wave responses, respectively, for all the landscape types, including forest, desert, and water. Furthermore, structure only played a decisive role in forest, while color played a major role in desert and water when using beta waves. Lastly, statistically significant differences between landscape groups and scenario groups with regard to alpha, beta, and gamma rhythms in brain waves were confirmed. The reasonable usage and layout of structure and color will have a very important guiding value for landscape aesthetics in future landscape design and landscape planning.
KW - Color
KW - Electroencephalography (EEG)
KW - Landscape recognition
KW - Structure
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U2 - 10.3390/ijerph18094866
DO - 10.3390/ijerph18094866
M3 - Article
C2 - 34063616
AN - SCOPUS:85104957583
SN - 1661-7827
VL - 18
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 9
M1 - 4866
ER -