Stochastic modeling of EEG rhythms with fractional Gaussian Noise. Karlekar, M. & Gupta, A. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 2520-2524, Sep., 2014.
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Paper abstract bibtex
Paper abstract bibtex 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.
@InProceedings{6952944,
author = {M. Karlekar and A. Gupta},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {Stochastic modeling of EEG rhythms with fractional Gaussian Noise},
year = {2014},
pages = {2520-2524},
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.},
keywords = {bioelectric potentials;brain;Brownian motion;discrete cosine transforms;electroencephalography;Gaussian noise;maximum likelihood estimation;medical signal processing;stochastic modeling;EEG rhythms;signal modeling;EEG signal rhythms;3-stage DCT based multirate filterbank;EEG signal decomposition;brain rhythm;delta rhythm;theta rhythm;alpha rhythm;beta rhythm;gamma rhythm;1st-order fractional Gaussian Noise;1st order fGn;2nd-order fGn processes;EEG signal nonstationarity;1st-order fractional Brownian motion;Hurst exponent;maximum likelihood estimation method;ML estimation method;public databases;discrete cosine transform;Electroencephalography;Discrete cosine transforms;Brain models;Brownian motion;Maximum likelihood estimation;Fractional Gaussian noise;EEG;DCT},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926449.pdf},
}Downloads: 0
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