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Cited by 448

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

EEG Electrode Sensitivity-An Application of Reciprocity

Rush, Stanley, Driscoll, Daniel A.·1969·IEEE Transactions on Biomedical Engineering

Abstract

In this paper, the reciprocity theorem is used to determine the sensitivity of EEG leads to the location and orientation of sources in the brain. Quantitative information used in determining the sensitivity is derived from constant potential plots of a three-concentric-sphere mathematical model of the head with current applied through surface leads (the reciprocal problem), and from an electrolytic tank employing a human skull. Advantages of the reciprocal or lead field approach are outlined and the following conclusions are drawn. 1) Leads placed at the end of a diameter through the center of the brain have a range of sensitivity due to source location of only 3 to 1. 2) For the same electrode placement, sensitivity is maximum to sources oriented parallel to the line of the electrodes regardless of source location. 3) Electrodes spaced 5 cm apart are about ten times more sensitive to proximal cortical sources (by virtue of position) than to sources near the center of the brain.

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Crossref · 10.1109/tbme.1969.4502598

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Metadata

Authors
Rush, S., & Driscoll, D. A.
Venue
IEEE Transactions on Biomedical Engineering
Published
1969-01
Volume
BME-16
Issue
1
Pages
15-22
ISSN
0018-9294 (ISSN-L)
OpenAlex
W2141723543
Cited by
448
References
9
FWCI
3.01
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: EEG Electrode Sensitivity-An Application of Reciprocity. https://app.cochat.ai/papers/10.1109/tbme.1969.4502598
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Rush, S., & Driscoll, D. A. (1969). EEG Electrode Sensitivity-An Application of Reciprocity. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.1969.4502598

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.