{"title":"基于径向基函数神经网络的脑电功率谱性能监测","authors":"B.P. Kirk, J. LaCourse","doi":"10.1109/NEBC.1997.594938","DOIUrl":null,"url":null,"abstract":"Length of vigilance is a major obstacle in jobs associated with low levels of arousal. To provide the highest levels of safety, the level of attention, particularly visual awareness, has to be monitored. A system has been designed, offline, as a precedent to a real-time awareness predictor. The electroencephalograph (EEG) is used as the major predictive data with a radial basis function network classifying the attention level.","PeriodicalId":393788,"journal":{"name":"Proceedings of the IEEE 23rd Northeast Bioengineering Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Performance monitoring from the EEG power spectrum with a radial basis function neural network\",\"authors\":\"B.P. Kirk, J. LaCourse\",\"doi\":\"10.1109/NEBC.1997.594938\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Length of vigilance is a major obstacle in jobs associated with low levels of arousal. To provide the highest levels of safety, the level of attention, particularly visual awareness, has to be monitored. A system has been designed, offline, as a precedent to a real-time awareness predictor. The electroencephalograph (EEG) is used as the major predictive data with a radial basis function network classifying the attention level.\",\"PeriodicalId\":393788,\"journal\":{\"name\":\"Proceedings of the IEEE 23rd Northeast Bioengineering Conference\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE 23rd Northeast Bioengineering Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEBC.1997.594938\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE 23rd Northeast Bioengineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEBC.1997.594938","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance monitoring from the EEG power spectrum with a radial basis function neural network
Length of vigilance is a major obstacle in jobs associated with low levels of arousal. To provide the highest levels of safety, the level of attention, particularly visual awareness, has to be monitored. A system has been designed, offline, as a precedent to a real-time awareness predictor. The electroencephalograph (EEG) is used as the major predictive data with a radial basis function network classifying the attention level.