{"title":"脑电信号中伽马响应的检测","authors":"D.I. Tufekci, S. Karakas, O. Arikan","doi":"10.1109/SIU.2006.1659834","DOIUrl":null,"url":null,"abstract":"In the detection of the existence of the early gamma response, subjective methods have been used. In this study, an automated gamma detection technique is developed based on the features obtained from the time-frequency representation of the EEG signal in the gamma frequency band. The technique easily discriminates the gamma response existing and non-existing cases for the generated synthetic data. The classification of the technique and that of the expert opinion coincide %77 for real EEG data","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Gamma Responses in EEG Signals\",\"authors\":\"D.I. Tufekci, S. Karakas, O. Arikan\",\"doi\":\"10.1109/SIU.2006.1659834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the detection of the existence of the early gamma response, subjective methods have been used. In this study, an automated gamma detection technique is developed based on the features obtained from the time-frequency representation of the EEG signal in the gamma frequency band. The technique easily discriminates the gamma response existing and non-existing cases for the generated synthetic data. The classification of the technique and that of the expert opinion coincide %77 for real EEG data\",\"PeriodicalId\":415037,\"journal\":{\"name\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2006.1659834\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the detection of the existence of the early gamma response, subjective methods have been used. In this study, an automated gamma detection technique is developed based on the features obtained from the time-frequency representation of the EEG signal in the gamma frequency band. The technique easily discriminates the gamma response existing and non-existing cases for the generated synthetic data. The classification of the technique and that of the expert opinion coincide %77 for real EEG data
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