Performance of the Electroencephalography Inverse Problem using Electric Potential Gradient Measurements

N. Von Ellenrieder; C. Muravchik; E. Spinelli; A. Nehorai; J. Roitman; W. Silva; S. Kochen

Performance of the Electroencephalography Inverse Problem using Electric Potential Gradient Measurements

N. Von Ellenrieder, C. Muravchik, E. Spinelli, A. Nehorai, J. Roitman, W. Silva, S. Kochen

Research output: Contribution to journal › Conference article › peer-review

Abstract

The EEC inverse problem is traditionally solved with measurements from an array of electric potential sensors. In this work we analyze the expected performance if electric potential gradient measurements were used to solve the inverse problem. We use the Cramér-Rao Bound (CRB) to analyze and compare the performance of the two cases. The spatial covariance of the noise is estimated from real EEC measurements.

Original language	English
Pages (from-to)	2814-2817
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	3
State	Published - 2003
Event	A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico Duration: Sep 17 2003 → Sep 21 2003

Keywords

Cramér-Rao Bound
Electroencephalography
Gradient sensor

Cite this

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title = "Performance of the Electroencephalography Inverse Problem using Electric Potential Gradient Measurements",

abstract = "The EEC inverse problem is traditionally solved with measurements from an array of electric potential sensors. In this work we analyze the expected performance if electric potential gradient measurements were used to solve the inverse problem. We use the Cram{\'e}r-Rao Bound (CRB) to analyze and compare the performance of the two cases. The spatial covariance of the noise is estimated from real EEC measurements.",

keywords = "Cram{\'e}r-Rao Bound, Electroencephalography, Gradient sensor",

author = "{Von Ellenrieder}, N. and C. Muravchik and E. Spinelli and A. Nehorai and J. Roitman and W. Silva and S. Kochen",

year = "2003",

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pages = "2814--2817",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "0589-1019",

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T1 - Performance of the Electroencephalography Inverse Problem using Electric Potential Gradient Measurements

AU - Von Ellenrieder, N.

AU - Muravchik, C.

AU - Spinelli, E.

AU - Nehorai, A.

AU - Roitman, J.

AU - Silva, W.

AU - Kochen, S.

PY - 2003

Y1 - 2003

N2 - The EEC inverse problem is traditionally solved with measurements from an array of electric potential sensors. In this work we analyze the expected performance if electric potential gradient measurements were used to solve the inverse problem. We use the Cramér-Rao Bound (CRB) to analyze and compare the performance of the two cases. The spatial covariance of the noise is estimated from real EEC measurements.

AB - The EEC inverse problem is traditionally solved with measurements from an array of electric potential sensors. In this work we analyze the expected performance if electric potential gradient measurements were used to solve the inverse problem. We use the Cramér-Rao Bound (CRB) to analyze and compare the performance of the two cases. The spatial covariance of the noise is estimated from real EEC measurements.

KW - Cramér-Rao Bound

KW - Electroencephalography

KW - Gradient sensor

UR - http://www.scopus.com/inward/record.url?scp=1542330803&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:1542330803

SN - 0589-1019

VL - 3

SP - 2814

EP - 2817

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 17 September 2003 through 21 September 2003

ER -

Performance of the Electroencephalography Inverse Problem using Electric Potential Gradient Measurements

Abstract

Keywords

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