{"title":"脑电生物信号识别的聚类分析","authors":"O. Georgieva, S. Milanov, P. Georgieva","doi":"10.1109/INISTA.2013.6577646","DOIUrl":null,"url":null,"abstract":"The paper aims to define the ability of unsupervised learning approach to identify emotional biosignals evoked while viewing affected pictures. Two problems are consequently resolved. First, the most important features of the Electroencephalography (EEG) data set have been selected. Secondly, cluster analysis technique is applied in order to extract the specific knowledge of the existing dependencies. The clustering results of particular data subsets are presented and discussed.","PeriodicalId":301458,"journal":{"name":"2013 IEEE INISTA","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Cluster analysis for EEG biosignal discrimination\",\"authors\":\"O. Georgieva, S. Milanov, P. Georgieva\",\"doi\":\"10.1109/INISTA.2013.6577646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper aims to define the ability of unsupervised learning approach to identify emotional biosignals evoked while viewing affected pictures. Two problems are consequently resolved. First, the most important features of the Electroencephalography (EEG) data set have been selected. Secondly, cluster analysis technique is applied in order to extract the specific knowledge of the existing dependencies. The clustering results of particular data subsets are presented and discussed.\",\"PeriodicalId\":301458,\"journal\":{\"name\":\"2013 IEEE INISTA\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE INISTA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INISTA.2013.6577646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE INISTA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INISTA.2013.6577646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper aims to define the ability of unsupervised learning approach to identify emotional biosignals evoked while viewing affected pictures. Two problems are consequently resolved. First, the most important features of the Electroencephalography (EEG) data set have been selected. Secondly, cluster analysis technique is applied in order to extract the specific knowledge of the existing dependencies. The clustering results of particular data subsets are presented and discussed.
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