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Spectral estimation with a non-parametric multiple-window method

Sandsten, Maria LU and Gänsler, Tomas (1993) 15th International Conference on IEEE Engineering in Medicine and Biology Society p.326-327
Abstract
The spectral characters of the electroencephalogram activity (EEG) is of interest both in studies of the EEG and in estimation oI Evoked Potentials. Often parametric methods assume an underlying model like AR or ARMA [1]. To avoid the model assumption a non-parametric multiple-window meihod for spectral estimation is used in this paper. The method uses Slepian's Discrete Prolate Spheroidal Wave Functions and produces estimates with low bias and high resolutlion even when the data length is short.
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
host publication
Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ
pages
326 - 327
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
15th International Conference on IEEE Engineering in Medicine and Biology Society
conference location
San Diego, United States
conference dates
1993-10-28 - 1993-10-31
external identifiers
  • scopus:0027842063
ISBN
0-7803-1377-1
DOI
10.1109/IEMBS.1993.978568
language
English
LU publication?
yes
id
ea5eb762-c7df-448c-af63-d8aa7ed24e98
date added to LUP
2018-03-23 16:24:19
date last changed
2021-01-03 05:23:30
@inproceedings{ea5eb762-c7df-448c-af63-d8aa7ed24e98,
  abstract     = {{The spectral characters of the electroencephalogram activity (EEG) is of interest both in studies of the EEG and in estimation oI Evoked Potentials. Often parametric methods assume an underlying model like AR or ARMA [1]. To avoid the model assumption a non-parametric multiple-window meihod for spectral  estimation is used in this paper. The method uses Slepian's Discrete Prolate Spheroidal Wave Functions and produces estimates with low bias and high resolutlion even when the data length is short.}},
  author       = {{Sandsten, Maria and Gänsler, Tomas}},
  booktitle    = {{Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ}},
  isbn         = {{0-7803-1377-1}},
  language     = {{eng}},
  pages        = {{326--327}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Spectral estimation with a non-parametric multiple-window method}},
  url          = {{http://dx.doi.org/10.1109/IEMBS.1993.978568}},
  doi          = {{10.1109/IEMBS.1993.978568}},
  year         = {{1993}},
}