{"title":"基于SVM-DS的脑警觉性评价","authors":"Meiyan Zhang, Jinwei Sun, Dan Liu, Qisong Wang","doi":"10.1109/ICCIA49625.2020.00032","DOIUrl":null,"url":null,"abstract":"Alertness (also called continuous attention) is a description of a person's ability to maintain attention over a period of time and make appropriate timely feedback to external stimuli. It includes three aspects: the degree of awakening, the concentration of attention and the ability to respond to emergencies. Many human-computer interaction positions, all require alertness maintaining a high level. The accurate assessment and estimation of alertness has become a hot topic in international research. Many researchers use electroencephalogram to evaluate drowsiness and wakefulness, finding that different levels of alertness correspond to different brain activities. This paper uses power spectral density and short-time Fourier transform to extract feature of the denoised brain signals, then proposes the method of Support Vector Machine-DS to evaluate brain alertness based on EEG.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Brain alertness evaluation based on SVM-DS\",\"authors\":\"Meiyan Zhang, Jinwei Sun, Dan Liu, Qisong Wang\",\"doi\":\"10.1109/ICCIA49625.2020.00032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Alertness (also called continuous attention) is a description of a person's ability to maintain attention over a period of time and make appropriate timely feedback to external stimuli. It includes three aspects: the degree of awakening, the concentration of attention and the ability to respond to emergencies. Many human-computer interaction positions, all require alertness maintaining a high level. The accurate assessment and estimation of alertness has become a hot topic in international research. Many researchers use electroencephalogram to evaluate drowsiness and wakefulness, finding that different levels of alertness correspond to different brain activities. This paper uses power spectral density and short-time Fourier transform to extract feature of the denoised brain signals, then proposes the method of Support Vector Machine-DS to evaluate brain alertness based on EEG.\",\"PeriodicalId\":237536,\"journal\":{\"name\":\"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIA49625.2020.00032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIA49625.2020.00032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Alertness (also called continuous attention) is a description of a person's ability to maintain attention over a period of time and make appropriate timely feedback to external stimuli. It includes three aspects: the degree of awakening, the concentration of attention and the ability to respond to emergencies. Many human-computer interaction positions, all require alertness maintaining a high level. The accurate assessment and estimation of alertness has become a hot topic in international research. Many researchers use electroencephalogram to evaluate drowsiness and wakefulness, finding that different levels of alertness correspond to different brain activities. This paper uses power spectral density and short-time Fourier transform to extract feature of the denoised brain signals, then proposes the method of Support Vector Machine-DS to evaluate brain alertness based on EEG.