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Brain signals : physics and mathematics of MEG and EEG

Author: Risto Ilmoniemi; Jukka Sarvas
Publisher: Cambridge : The MIT Press, 2019
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
A unified treatment of the generation and analysis of brain-generated electromagnetic fields. In Brain Signals, Risto Ilmoniemi and Jukka Sarvas present the basic physical and mathematical principles of magnetoencephalography (MEG) and electroencephalography (EEG), describing what kind of information is available in the neuroelectromagnetic field and how the measured MEG and EEG signals can be analyzed. Unlike most  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ilmoniemi, Risto (Risto J.).
Brain signals
(DLC) 2018036973
(OCoLC)1054366278
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Risto Ilmoniemi; Jukka Sarvas
ISBN: 9780262352819 0262352818
OCLC Number: 1082365476
Description: 1 online resource (248 pages)
Contents: Intro; Contents; Preface; 1. Introduction; 1.1 The Brain; 1.2 Brain States; 1.3 Electrical States of the Brain; 1.4 MEG and EEG Signals as Measures of Brain States; 1.5 Historical and Technical Background of EEG; 1.6 Historical and Technical Background of MEG; 1.7 State of the Art and Future Prospects for MEG Technology; 1.7.1 High-Tc SQUID Magnetometers; 1.7.2 Optically Pumped Magnetometers; 1.7.3 hyQUIDs; 1.7.4 Hybrid MEG-MRI Systems; 1.7.5 MEG Systems for Measuring Infants and Fetuses; 1.7.6 Methods to Remove Unwanted Components in the Data; 2. Genesis of MEG and EEG 2.1 Maxwell's Equations2.1.1 Impressed and Primary Currents; 2.2 The Current Dipole and Lead Fields; 2.3 Cellular Basis for Electromagnetic Fields; 2.4 The Action Potential; 2.5 Postsynaptic Potential (PSP); 2.6 Description of Synaptic Activity; 3. Forward Problem; 3.1 Conductor Models; 3.2 The General Case; 3.3 Fields in an Infinite Homogeneous Medium; 3.4 Fields in an Inhomogeneous Medium; 3.5 Computing the Lead Field; 3.6 Spherical Model; 3.7 Magnetic Field for the Spherical Model in Cartesian Coordinates; 3.8 Magnetic Field for the Spherical Model in Spherical Coordinates 3.9 Triangle Construction and the Vector Potential of the Magnetic Field in the Spherical Model3.10 Electric Potential V in a Layered Sphere and in a Homogeneous Sphere; 3.11 Semi-Infinite Homogeneous Conductor; 3.12 Appendix: Vector Potential Outside a Spherical Conductor Due to Current Dipole in the Conductor; 3.13 Appendix: Series Expansion for Potential Due to Current Dipole in Multilayered Sphere; 4. Review of Linear Algebra and Probability Theory for MEG and EEG Data Analysis; 4.1 Notation and Terminology; 4.2 Review of Linear Algebra for MEG and EEG 4.3 Review of Elementary Probability Theory4.4 Solving Noisy Linear Equations; 4.5 Solving Noisy Equations with Estimators; 4.6 MNLS Solution and Tikhonov Regularization in Other Norms; 5. Interpreting MEG and EEG Data; 5.1 Approaches to the Interpretation of MEG and EEG Data; 5.2 The Inverse Problem; 5.3 The Solution Without A Priori Information; 5.4 Signal Space and Signal-Space Projection (SSP); 5.5 The Solution If There Is A Priori Information; 5.6 Measurement Data; 5.7 Search for a Single Dipole Source; 5.8 The EEG/MEG Inverse Problem and Its Solution by the MNE Method 5.8.1 Noise-Normalized MNE Methods5.8.2 Minimum-Norm Estimates with Other Norms; 6. Beamformers; 6.1 Measurement Data Matrix for Beamformers; 6.1.1 Signal-to-Noise Ratio (SNR); 6.2 Scalar Beamformer; 6.3 Scalar Beamformer Filter Vector with Noiseless Data and Uncorrelated Time Courses; 6.4 Filter Vector with Correlated Time Courses and Noiseless or Noisy Data; 6.5 Linear Transform of the Data Equation; 6.6 Search for Source Dipole Locations with the Output Power µ(p); 6.7 Improved Beamformer Localizers for Searching Source Dipoles; 6.7.1 Regularizing Data and Noise Covariance Matrices
Responsibility: Risto Ilmoniemi and Jukka Sarvas.

Abstract:

A unified treatment of the generation and analysis of brain-generated electromagnetic fields.  Read more...

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