Influence of skull inhomogeneities on EEG source localization

We investigated the influence of using simplified models of the skull on electroencephalogram (EEG) source localization. An accurately segmented skull from computed tomography (CT) images, including spongy and compact bones as well as some air–filled cavities, was used as a reference model. The simplified models approximated the skull as a homogeneous compartment with: (1) isotropic, and (2) anisotropic conductivity. The results showed that these approximations could lead to errors of more than 2 cm in dipole estimation. We recommend the use of anisotropy but considering a different ratio for each region of the skull, according to the amount of spongy bone

Spatiotemporal dynamics of feature-based attention spread: evidence from combined electroencephalographic and magnetoencephalographic recordings

Attentional selection on the basis of nonspatial stimulus features induces a sensory gain enhancement by increasing the firing-rate of individual neurons tuned to the attended feature, while responses of neurons tuned to opposite feature-values are suppressed. Here we recorded event-related potentials (ERPs) and magnetic fields (ERMFs) in human observers to investigate the underlying neural correlates of feature-based attention at the population level. During the task subjects attended to a moving transparent surface presented in the left visual field, while task-irrelevant probe stimuli executing brief movements into varying directions were presented in the opposite visual field. ERP and ERMF amplitudes elicited by the unattended task-irrelevant probes were modulated as a function of the similarity between their movement direction and the task-relevant movement direction in the attended visual field. These activity modulations reflecting globally enhanced processing of the attended feature were observed to start not before 200 ms poststimulus and were localized to the motion-sensitive area hMT. The current results indicate that feature-based attention operates in a global manner but needs time to spread and provide strong support for the feature-similarity gain model

Magnetoelectric Sensor Systems and Applications

In the field of magnetic sensing, a wide variety of different magnetometer and gradiometer sensor types, as well as the corresponding read-out concepts, are available. Well-established sensor concepts such as Hall sensors and magnetoresistive sensors based on giant magnetoresistances (and many more) have been researched for decades. The development of these types of sensors has reached maturity in many aspects (e.g., performance metrics, reliability, and physical understanding), and these types of sensors are established in a large variety of industrial applications. Magnetic sensors based on the magnetoelectric effect are a relatively new type of magnetic sensor. The potential of magnetoelectric sensors has not yet been fully investigated. Especially in biomedical applications, magnetoelectric sensors show several advantages compared to other concepts for their ability, for example, to operate in magnetically unshielded environments and the absence of required cooling or heating systems. In recent years, research has focused on understanding the different aspects influencing the performance of magnetoelectric sensors. At Kiel University, Germany, the Collaborative Research Center 1261 “Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics”, funded by the German Research Foundation, has dedicated its work to establishing a fundamental understanding of magnetoelectric sensors and their performance parameters, pushing the performance of magnetoelectric sensors to the limits and establishing full magnetoelectric sensor systems in biological and clinical practice

Single Epoch Analysis and Bi-hemisphere Study of Magnetoencephalographic (MEG) Signals using Vector Signal Transformation V3 and Magnetic Field Tomography (MFT)

The biomagnetic inverse problem has no unique solution, nevertheless even a cursory look at the features shown in raw signal can often suffice to highlight strong superficial activity. To do a proper single epoch analysis is normally prohibitively expensive in terms of computing demands. Hence the original aim of this thesis was to use simple efficient signal transformations to characterize superficial generators and contrast the single epoch signature with that extracted from the average signal. The results have intrigued us sufficiently to go beyond the original goal and extract very preliminary estimates of activity across the cerebral hemisphere in single trials. The original tool, and one that we have used for much of the work, is a simple vector signal transformation called V3. This signal transformation highlights nearby sources; it is a crude but quick estimator of generators directly from the raw MEG signals. Together with Magnetic Field Tomography (MFT), which relies on distributed source analysis of the MEG signals, we have tackled the following specific problems relating to aspects of normal brain function: efficient estimation of generators of magnetic fields; relationship between the average signal and single trials; and interhemispheric differences and relationship between the activity in the left and right hemispheres of the brain. During the project, we have used as examples auditory evoked MEG measurements obtained from two multichannel systems and applied the V3 and MFT analysis to both the average and single trial signals. In particular, we chose the 40-Hz (or gamma band) auditory response as the study subject. We found that in single epochs similar patterns of high frequency activity are observed in the area around the auditory cortex well before, close to and well after stimulus onset; the sequence of events observed in the average can only represent the evolution of events in single trials in a statistical way; and deep and central areas of the brain may be the seeds for the main deflections observed in the auditory responses

Numerical modeling in electro- and magnetoencephalography

This Thesis concerns the application of two numerical methods, Boundary Element Method (BEM) and Finite Element Method (FEM) to forward problem solution of bioelectromagnetic source localization in the brain. The aim is to improve the accuracy of the forward problem solution in estimating the electrical activity of the human brain from electric and magnetic field measurements outside the head. Electro- and magnetoencephalography (EEG, MEG) are the most important tools enabling us to gather knowledge about the human brain non-invasively. This task is alternatively named brain mapping. An important step in brain mapping is determining from where the brain signals originate. Using appropriate mathematical models, a localization of the sources of measured signals can be performed. A general motivation of this work was the fact that source localization accuracy can be improved by solving the forward problem with higher accuracy. In BEM studies, accurate representation of model geometry using higher order elements improves the solution of the forward problem. In FEM, complex conductivity information can be incorporated into numerical model. Using Whitney-type finite elements instead of using singular sources such as point dipoles, primary and volume currents are represented as continuous sources. With comparison to analytical solutions available in simple geometries such as sphere, the studied numerical methods show improvements in the forward problem solution of bioelectromagnetic source imaging.reviewe

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

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.Peer reviewe

Recent advances in modeling and analysis of bioelectric and biomagnetic sources

Determining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain function and the nature of cardiovascular diseases requires sophisticated methods applicable to non-invasively measured bioelectric and biomagnetic data. As it is difficult to solve for all unknown parameters at once, several strains of data analysis have been developed, each trying to solve a different part of the problem and each requiring a different set of assumptions. Current trends and results from major topics of electro- and magnetoencephalographic data analysis are presented here together with the aim of stimulating research into the unification of the different approaches. The following topics are discussed: source reconstruction using detailed finite element modeling to locate sources deep in cthe brain; connectivity analysis for the quantification of strength and direction of information flow between activity centers, preferably incorporating an inverse solution; the conflict between the statistical independence assumption of sources and a possible connectivity; the verification and validation of results derived from non-invasively measured data through animal studies and phantom measurements. This list already indicates the benefits of a unified view