{"title":"无损脑电信号压缩","authors":"A. Gumusalan, Z. Arnavut, H. Kocak","doi":"10.1109/ICSCCW.2009.5379436","DOIUrl":null,"url":null,"abstract":"In this work, we study the lossless compression of EEG (electroencephalograph) signals using linear prediction and arithmetic coder. We show that, when we separate the less significant bits of each signal, linear prediction techniques yield better prediction, and with a structured arithmetic coder not only our technique achieves better compression rates than other techniques reported previously, but also our technique is much faster than the others.","PeriodicalId":218020,"journal":{"name":"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Losless EEG signal compression\",\"authors\":\"A. Gumusalan, Z. Arnavut, H. Kocak\",\"doi\":\"10.1109/ICSCCW.2009.5379436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we study the lossless compression of EEG (electroencephalograph) signals using linear prediction and arithmetic coder. We show that, when we separate the less significant bits of each signal, linear prediction techniques yield better prediction, and with a structured arithmetic coder not only our technique achieves better compression rates than other techniques reported previously, but also our technique is much faster than the others.\",\"PeriodicalId\":218020,\"journal\":{\"name\":\"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSCCW.2009.5379436\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCCW.2009.5379436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this work, we study the lossless compression of EEG (electroencephalograph) signals using linear prediction and arithmetic coder. We show that, when we separate the less significant bits of each signal, linear prediction techniques yield better prediction, and with a structured arithmetic coder not only our technique achieves better compression rates than other techniques reported previously, but also our technique is much faster than the others.