{"title":"基于PARAFAC和Tucker模型的多通道脑电数据三向分析","authors":"Z. Rost'áková, R. Rosipal","doi":"10.23919/MEASUREMENT47340.2019.8780005","DOIUrl":null,"url":null,"abstract":"Changes of hidden sources of the neural electrical activity of a brain over time, as represented by a continuously recorded multichannel electroencephalogram (EEG) at the scalp, can be detected by tensor or multiway decomposition of the EEG records. In this study, the performance of i) the constrained Tucker model and ii) the parallel factor analysis (PARAFAC) models are compared on real EEG data.","PeriodicalId":129350,"journal":{"name":"2019 12th International Conference on Measurement","volume":"401 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Three-way Analysis of Multichannel EEG Data Using the PARAFAC and Tucker Models\",\"authors\":\"Z. Rost'áková, R. Rosipal\",\"doi\":\"10.23919/MEASUREMENT47340.2019.8780005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Changes of hidden sources of the neural electrical activity of a brain over time, as represented by a continuously recorded multichannel electroencephalogram (EEG) at the scalp, can be detected by tensor or multiway decomposition of the EEG records. In this study, the performance of i) the constrained Tucker model and ii) the parallel factor analysis (PARAFAC) models are compared on real EEG data.\",\"PeriodicalId\":129350,\"journal\":{\"name\":\"2019 12th International Conference on Measurement\",\"volume\":\"401 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 12th International Conference on Measurement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/MEASUREMENT47340.2019.8780005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 12th International Conference on Measurement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/MEASUREMENT47340.2019.8780005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Three-way Analysis of Multichannel EEG Data Using the PARAFAC and Tucker Models
Changes of hidden sources of the neural electrical activity of a brain over time, as represented by a continuously recorded multichannel electroencephalogram (EEG) at the scalp, can be detected by tensor or multiway decomposition of the EEG records. In this study, the performance of i) the constrained Tucker model and ii) the parallel factor analysis (PARAFAC) models are compared on real EEG data.
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