Magnetoencephalography for brain electrophysiology and imaging-Reference-Cited by-同舟云学术

Magnetoencephalography for brain electrophysiology and imaging

Published:2017-02-23 Issue:3 Volume:20 Page:327-339
ISSN:1097-6256
Container-title:Nature Neuroscience
language:en
Short-container-title:Nat Neurosci

Author:

Baillet Sylvain

Publisher

Springer Science and Business Media LLC

Subject

General Neuroscience

Link

http://www.nature.com/articles/nn.4504.pdf

Reference142 articles.

1. Cohen, D. Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science 161, 784–786 (1968). The first demonstration of brain magnetic fields measured outside the human scalp. The core principle of using a specially designed multilayer magnetically shielded chamber is introduced. Signal averaging reduced environmental magnetic noise and revealed modulations of alpha rhythms related to eyes open/closed.

2. Cohen, D. Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science 175, 664–666 (1972). The seminal demonstration that SQUID superconducting detectors greatly improved the sensitivity of MEG. Also features simultaneous EEG and MEG recordings with the first discussion concerning their respective merits. Also first recording of MEG patient data.

3. Hämäläinen, M., Hari, R., Ilmoniemi, R., Knuutila, J. & Lounasmaa, O. Magnetoencephalography: theory, instrumentation and applications to the noninvasive study of human brain function. Rev. Mod. Phys. 65, 413–497 (1993). A comprehensive and authoritative review of the technique, with important details concerning instrumentation, the physics and models. Some 25 years after, this reference remains a must-read.

4. Feynman, R.P. The Feynman Lectures on Physics Vol. 2 (Addison-Wesley, Reading, Massachusetts, 1964).A classic textbook on electromagnetism that remains a reference in content and clarity of style.

5. Baillet, S., Mosher, J. & Leahy, R. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001). MEG and EEG methods and models explained from an electrical engineering perspective.

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