Spectral estimation with a non-parametric multiple-window method

Maria Sandsten; Tomas Gänsler

doi:10.1109/IEMBS.1993.978568

Spectral estimation with a non-parametric multiple-window method

Maria Sandsten, Tomas Gänsler

Statistical Signal Processing Group

Lund University

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	326-327
ISBN (Print)	0-7803-1377-1
DOIs	https://doi.org/10.1109/IEMBS.1993.978568
Publication status	Published - 1993
Event	15th International Conference on IEEE Engineering in Medicine and Biology Society - San Diego, United States Duration: 1993 Oct 28 → 1993 Oct 31

Conference

Conference	15th International Conference on IEEE Engineering in Medicine and Biology Society
Country/Territory	United States
City	San Diego
Period	1993/10/28 → 1993/10/31

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10.1109/IEMBS.1993.978568

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@inproceedings{ea5eb762c7df448caf63d8aa7ed24e98,

title = "Spectral estimation with a non-parametric multiple-window method",

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 = "Maria Sandsten and Tomas G{\"a}nsler",

year = "1993",

doi = "10.1109/IEMBS.1993.978568",

language = "English",

isbn = "0-7803-1377-1 ",

pages = "326--327",

booktitle = "Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "15th International Conference on IEEE Engineering in Medicine and Biology Society ; Conference date: 28-10-1993 Through 31-10-1993",

}

Sandsten, M & Gänsler, T 1993, Spectral estimation with a non-parametric multiple-window method. in Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ . IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 326-327, 15th International Conference on IEEE Engineering in Medicine and Biology Society , San Diego, United States, 1993/10/28. https://doi.org/10.1109/IEMBS.1993.978568

Spectral estimation with a non-parametric multiple-window method. / Sandsten, Maria; Gänsler, Tomas.
Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ . IEEE - Institute of Electrical and Electronics Engineers Inc., 1993. p. 326-327.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Spectral estimation with a non-parametric multiple-window method

AU - Sandsten, Maria

AU - Gänsler, Tomas

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

U2 - 10.1109/IEMBS.1993.978568

DO - 10.1109/IEMBS.1993.978568

M3 - Paper in conference proceeding

SN - 0-7803-1377-1

SP - 326

EP - 327

BT - Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on IEEE Engineering in Medicine and Biology Society

Y2 - 28 October 1993 through 31 October 1993

ER -

Spectral estimation with a non-parametric multiple-window method

Abstract

Conference

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