{"title":"EEG signal analysis for human workload classification","authors":"C. Ling, H. Goins, A. Ntuen, R. Li","doi":"10.1109/SECON.2001.923101","DOIUrl":null,"url":null,"abstract":"This paper provides the results of determining the state of a human pilot operator by using electroencephalograph (EEG) data. The state of a human operator is used to represent the mental (cognitive) workload experienced during task execution. This study used EEG data gathered from a crew-simulation laboratory environment. By using EEG data from twelve subjects encountering six simulated pilot workload levels, we set up a neural network to obtain an overall mean classification accuracy of over 80%. A comparison between the conventional backpropagation method and the resilient backpropagation method also shows that a significant reduction in training time can be achieved.","PeriodicalId":368157,"journal":{"name":"Proceedings. IEEE SoutheastCon 2001 (Cat. No.01CH37208)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE SoutheastCon 2001 (Cat. No.01CH37208)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2001.923101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
This paper provides the results of determining the state of a human pilot operator by using electroencephalograph (EEG) data. The state of a human operator is used to represent the mental (cognitive) workload experienced during task execution. This study used EEG data gathered from a crew-simulation laboratory environment. By using EEG data from twelve subjects encountering six simulated pilot workload levels, we set up a neural network to obtain an overall mean classification accuracy of over 80%. A comparison between the conventional backpropagation method and the resilient backpropagation method also shows that a significant reduction in training time can be achieved.