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Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

Author

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  • Yuting Wang

    (Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Shujian Wang

    (Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China)

  • Ming Xu

    (Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China
    College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
    BNU-HKUST Laboratory for Green Innovation, Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, China)

Abstract

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

Suggested Citation

  • Yuting Wang & Shujian Wang & Ming Xu, 2022. "Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features," IJERPH, MDPI, vol. 19(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:629-:d:719115
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    References listed on IDEAS

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    1. E. Yu. Kolbovsky & U. A. Medovikova, 2017. "Assessment of the aesthetic landscape properties for managing areas of outstanding natural beauty and historical significance," Regional Research of Russia, Springer, vol. 7(1), pages 81-88, January.
    2. Sara Tilley & Chris Neale & Agnès Patuano & Steve Cinderby, 2017. "Older People’s Experiences of Mobility and Mood in an Urban Environment: A Mixed Methods Approach Using Electroencephalography (EEG) and Interviews," IJERPH, MDPI, vol. 14(2), pages 1-20, February.
    3. Yuting Wang & Ming Xu, 2021. "Electroencephalogram Application for the Analysis of Stress Relief in the Seasonal Landscape," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
    4. Yuting Wang & Shujian Wang & Ming Xu, 2021. "The Function of Color and Structure Based on EEG Features in Landscape Recognition," IJERPH, MDPI, vol. 18(9), pages 1-14, May.
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    1. Xiaojie Huang & Xiangtao Sun & Lijun Zhang & Tong Zhu & Hao Yang & Qingsong Xiong & Lijie Feng, 2022. "A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal," IJERPH, MDPI, vol. 19(22), pages 1-16, November.

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