干旱区生态环境知识资源中心(Arid): Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

Arid

DOI	10.3390/ijerph19020629
	Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features
	Wang, Yuting; Wang, Shujian; Xu, Ming
通讯作者	Xu, M (corresponding author)，Henan Univ, Henan Key Lab Earth Syst Observ & Modeling, Kaifeng 475004, Peoples R China. ; Xu, M (corresponding author)，Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China. ; Xu, M (corresponding author)，Beijing Normal Univ Zhuhai, Adv Inst Nat Sci, BNU HKUST Lab Green Innovat, Zhuhai 519087, Peoples R China.
来源期刊	INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
EISSN	1660-4601
出版年	2022
卷号	19 期号:2
英文摘要	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.
英文关键词	unmanned aerial vehicle (UAV) electroencephalogram (EEG) features landscape perception machine learning
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000757574700001
WOS关键词	DIFFERENTIAL ENTROPY FEATURE ; GUIDELINES ; AESTHETICS ; JUDGMENTS ; RESPONSES ; SELECTION
WOS类目	Environmental Sciences ; Public, Environmental & Occupational Health
WOS研究方向	Environmental Sciences & Ecology ; Public, Environmental & Occupational Health
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376664
作者单位	[Wang, Yuting; Wang, Shujian; Xu, Ming] Henan Univ, Henan Key Lab Earth Syst Observ & Modeling, Kaifeng 475004, Peoples R China; [Wang, Yuting; Wang, Shujian; Xu, Ming] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China; [Xu, Ming] Beijing Normal Univ Zhuhai, Adv Inst Nat Sci, BNU HKUST Lab Green Innovat, Zhuhai 519087, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yuting,Wang, Shujian,Xu, Ming. Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features[J],2022,19(2).
APA	Wang, Yuting,Wang, Shujian,&Xu, Ming.(2022).Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,19(2).
MLA	Wang, Yuting,et al."Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 19.2(2022).