{"title":"含分数高斯噪声的脑电图节律随机建模","authors":"Mandar Karlekar, Anubha Gupta","doi":"10.5281/ZENODO.44212","DOIUrl":null,"url":null,"abstract":"This paper presents a novel approach to signal modeling for EEG signal rhythms. A new method of 3-stage DCT based multirate filterbank is proposed for the decomposition of EEG signals into brain rhythms: delta, theta, alpha, beta, and gamma rhythms. It is shown that theta, alpha, and gamma rhythms can be modeled as 1st order fractional Gaussian Noise (fGn), while the beta rhythms can be modeled as 2nd order fGn processes. These fGn processes are stationary random processes. Further, it is shown that the delta subband imbibes all the nonstationarity of EEG signals and can be modeled as a 1st order fractional Brownian motion (fBm) process. The modeling of subbands is characterized by Hurst exponent, estimated using maximum likelihood (ML) estimation method. The modeling approach has been tested on two public databases.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Stochastic modeling of EEG rhythms with fractional Gaussian Noise\",\"authors\":\"Mandar Karlekar, Anubha Gupta\",\"doi\":\"10.5281/ZENODO.44212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel approach to signal modeling for EEG signal rhythms. A new method of 3-stage DCT based multirate filterbank is proposed for the decomposition of EEG signals into brain rhythms: delta, theta, alpha, beta, and gamma rhythms. It is shown that theta, alpha, and gamma rhythms can be modeled as 1st order fractional Gaussian Noise (fGn), while the beta rhythms can be modeled as 2nd order fGn processes. These fGn processes are stationary random processes. Further, it is shown that the delta subband imbibes all the nonstationarity of EEG signals and can be modeled as a 1st order fractional Brownian motion (fBm) process. The modeling of subbands is characterized by Hurst exponent, estimated using maximum likelihood (ML) estimation method. The modeling approach has been tested on two public databases.\",\"PeriodicalId\":198408,\"journal\":{\"name\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.44212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.44212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stochastic modeling of EEG rhythms with fractional Gaussian Noise
This paper presents a novel approach to signal modeling for EEG signal rhythms. A new method of 3-stage DCT based multirate filterbank is proposed for the decomposition of EEG signals into brain rhythms: delta, theta, alpha, beta, and gamma rhythms. It is shown that theta, alpha, and gamma rhythms can be modeled as 1st order fractional Gaussian Noise (fGn), while the beta rhythms can be modeled as 2nd order fGn processes. These fGn processes are stationary random processes. Further, it is shown that the delta subband imbibes all the nonstationarity of EEG signals and can be modeled as a 1st order fractional Brownian motion (fBm) process. The modeling of subbands is characterized by Hurst exponent, estimated using maximum likelihood (ML) estimation method. The modeling approach has been tested on two public databases.