Literature DB >> 21912974

Comparison of the properties of EEG and MEG in detecting the electric activity of the brain.

Jaakko Malmivuo1.   

Abstract

Since the detection of the first biomagnetic signals in 1963 there has been continuous discussion on the properties and relative merits of bioelectric and biomagnetic measurements. In this review article it is briefly discussed the early history of this controversy. Then the theory of the independence and interdependence of bioelectric and biomagnetic signals is explained, and a clinical study on ECG and MCG that strongly supports this theory is presented. The spatial resolutions of EEG and MEG are compared in detail, and the issue of the maximum number of electrodes in EEG is also discussed. Finally, some special properties of EEG and MEG methods are described. In brief, the conclusion is that EEG and MEG are only partially independent and their spatial resolutions are about the same. Recording both of them brings some additional information on the bioelectric activity of the brain. These two methods have certain unique properties that make either of them more beneficial in certain applications.

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Year:  2011        PMID: 21912974     DOI: 10.1007/s10548-011-0202-1

Source DB:  PubMed          Journal:  Brain Topogr        ISSN: 0896-0267            Impact factor:   3.020


  8 in total

1.  A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.

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3.  Defining Surgical Terminology and Risk for Brain Computer Interface Technologies.

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Journal:  Front Neurosci       Date:  2021-03-26       Impact factor: 4.677

4.  Neural-Behavioral Relation in Phonetic Discrimination Modulated by Language Background.

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Journal:  Brain Sci       Date:  2022-03-29

5.  Spatiotemporal Signatures of Surprise Captured by Magnetoencephalography.

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6.  Electric Field Encephalography as a tool for functional brain research: a modeling study.

Authors:  Yury Petrov; Srinivas Sridhar
Journal:  PLoS One       Date:  2013-07-03       Impact factor: 3.240

7.  Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG.

Authors:  Martin Hardmeier; Florian Hatz; Habib Bousleiman; Christian Schindler; Cornelis Jan Stam; Peter Fuhr
Journal:  PLoS One       Date:  2014-10-06       Impact factor: 3.240

8.  Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm.

Authors:  Maren Stropahl; Anna-Katharina R Bauer; Stefan Debener; Martin G Bleichner
Journal:  Front Neurosci       Date:  2018-05-08       Impact factor: 4.677

  8 in total

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