Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatial discrimination is, under favorable circumstances, 2-3 mm for sources in the cerebral cortex. In MEG studies, the weak 10 fT-1 pT magnetic fields produced by electric currents flowing in neurons are measured with multichannel SQUID (superconducting quantum interference device) gradiometers. The sites in the cerebral cortex that are activated by a stimulus can be found from the detected magnetic-field distribution, provided that appropriate assumptions about the source render the solution of the inverse problem unique. Many interesting properties of the working human brain can be studied, including spontaneous activity and signal processing following external stimuli. For clinical purposes, determination of the locations of epileptic foci is of interest. The authors begin with a general introduction and a short discussion of the neural basis of MEG. The mathematical theory of the method is then explained in detail, followed by a thorough description of MEG instrumentation, data analysis, and practical construction of multi-SQUID devices. Finally, several MEG experiments performed in the authors' laboratory are described, covering studies of evoked responses and of spontaneous activity in both healthy and diseased brains. Many MEG studies by other groups are discussed briefly as well.

/pdf/magnetoencephalography-theory-instrumentation-and-1oezfk882m.pdf

Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain

The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Two decades of array signal processing research: the parametric approach

The event-related potential (ERP) technique in cognitive neuroscience allows scientists to observe human brain activity that reflects specific cognitive processes. In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches. The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

/pdf/an-introduction-to-the-event-related-potential-technique-38ksmp4fjp.pdf

An Introduction to the Event-Related Potential Technique

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

/pdf/brainstorm-a-user-friendly-application-for-meg-eeg-analysis-3x9ksstzul.pdf

Brainstorm: a user-friendly application for MEG/EEG analysis

A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

The authors present general descriptive models for spatiotemporal MEG (magnetoencephalogram) data and show the separability of the linear moment parameters and nonlinear location parameters in the MEG problem. A forward model with current dipoles in a spherically symmetric conductor is used as an example: however, other more advanced MEG models, as well as many EEG (electroencephalogram) models, can also be formulated in a similar linear algebra framework. A subspace methodology and computational approach to solving the conventional least-squares problem is presented. A new scanning approach, equivalent to the statistical MUSIC method, is also developed. This subspace method scans three-dimensional space with a one-dipole model, making it computationally feasible to scan the complete head volume. >

Multiple dipole modeling and localization from spatio-temporal MEG data

A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods. This formulation is achieved through factorization of the lead field into the product of the moment of the elemental current dipole source with a "kernel matrix" that depends on the head geometry and source and sensor locations, and a "sensor matrix" that models sensor orientation and gradiometer effects in MEG and differential measurements in EEG. Using this formulation and a recently developed approximation formula for EEG, based on the "Berg parameters", we present novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG. We also present novel investigations of different boundary element methods (BEMs) and present evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods. Explicit expressions for the matrix kernels for MEG and EEG for spherical and realistic head geometries are included.

/pdf/eeg-and-meg-forward-solutions-for-inverse-methods-5f9oygs4a9.pdf

EEG and MEG: forward solutions for inverse methods

https://neuroimage.usc.edu/paperspdf/1993_Mosher_EEGJrnl_CRLBs.pdf

Error bounds for EEG and MEG dipole source localization

An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magne- toencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in re- sponse to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large numer of neurons lo- cated in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experi- ments is the localization of these neural current dipoles. We present here a linear algebraic framework for three common spatio-temporal dipole models: i) unconstrained dipoles, ii) di- poles with a fixed location, and iii) dipoles with a fixed orien- tation and location. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. With a common model, we show how the parameter estimation prob- lem may be decomposed into the estimation of the time invari- ant parameters using nonlinear least-squares minimization, followed by linear estimation of the associated time varying pa- rameters. A subspace formulation is presented and used to de- rive a suboptimal least-squares subspace scanning method. The resulting algorithm is a special case of the well-known Multiple SIgnal Classification (MUSIC) method, in which the solution (multiple dipole locations) is found by scanning potential loca- tions using a simple one dipole model. Principal components analysis (PCA) dipole fitting has also been used to individually fit single dipoles in a multiple dipole problem. Analysis is pre- sented here to show why PCA dipole fitting will fail in general, whereas the subspace method presented here will generally suc- ceed. Numerically efficient means of calculating the cost func- tions are presented, and problems of model order selection and missing moments are discussed. Results from a simulation and a somatosensory experiment are presented.

http://ieeexplore.ieee.org/iel5/10/3788/00141192.pdf

P.S. Lewis

Papers

Multiple dipole modeling and localization from spatio-temporal MEG data

EEG and MEG: forward solutions for inverse methods

Error bounds for EEG and MEG dipole source localization

Multiple Dipole Modeling and Localization from

A neuromagnetic study of selective auditory attention.