http://web.mit.edu/~ppurdon/www/source-imaging.pdf

EEG source imaging

Transcranial direct current stimulation (tDCS) provides a non-invasive tool to elicit neuromodulation by delivering current through electrodes placed on the scalp. The present clinical paradigm uses two relatively large electrodes to inject current through the head resulting in electric fields that are broadly distributed over large regions of the brain. In this paper, we present a method that uses multiple small electrodes (i.e. 1.2 cm diameter) and systematically optimize the applied currents to achieve effective and targeted stimulation while ensuring safety of stimulation. We found a fundamental trade-off between achievable intensity (at the target) and focality, and algorithms to optimize both measures are presented. When compared with large pad-electrodes (approximated here by a set of small electrodes covering 25 cm(2)), the proposed approach achieves electric fields which exhibit simultaneously greater focality (80% improvement) and higher target intensity (98% improvement) at cortical targets using the same total current applied. These improvements illustrate the previously unrecognized and non-trivial dependence of the optimal electrode configuration on the desired electric field orientation and the maximum total current (due to safety). Similarly, by exploiting idiosyncratic details of brain anatomy, the optimization approach significantly improves upon prior un-optimized approaches using small electrodes. The analysis also reveals the optimal use of conventional bipolar montages: maximally intense tangential fields are attained with the two electrodes placed at a considerable distance from the target along the direction of the desired field; when radial fields are desired, the maximum-intensity configuration consists of an electrode placed directly over the target with a distant return electrode. To summarize, if a target location and stimulation orientation can be defined by the clinician, then the proposed technique is superior in terms of both focality and intensity as compared to previous solutions and is thus expected to translate into improved patient safety and increased clinical efficacy.

https://iopscience.iop.org/article/10.1088/1741-2560/8/4/046011/pdf

Optimized multi-electrode stimulation increases focality and intensity at target.

Background
The aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes.

/pdf/review-on-solving-the-forward-problem-in-eeg-source-analysis-4susg04pdg.pdf

Review on solving the forward problem in EEG source analysis

The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.

EEG Source Imaging: A Practical Review of the Analysis Steps.

Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (ie, the forward model) Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model We evaluated its accuracy using the crosstalk and point spread metrics The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations The point spread metric provides the complementary measure: for that same location, the point spread describes the mis-localization of activity from that specified location to other locations in the brain We also propose and examine the utility of a "noise sensitivity normalized" inverse operator Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread

/pdf/monte-carlo-simulation-studies-of-eeg-and-meg-localization-4co08o4s0x.pdf

Monte Carlo simulation studies of EEG and MEG localization accuracy.

It is generally believed that because the skull has low conductivity to electric current but is transparent to magnetic fields, the measurement sensitivity of the magnetoencephalography (MEG) in the brain region should be more concentrated than that of the electroencephalography (EEG). It is also believed that the information recorded by these techniques is very different. If this were indeed the case, it might be possible to justify the cost of MEG instrumentation which is at least 25 times higher than that of EEG instrumentation. The localization of measurement sensitivity using these techniques was evaluated quantitatively in an inhomogeneous spherical head model using a new concept called half-sensitivity volume (HSV). It is shown that the planar gradiometer has a far smaller HSV than the axial gradiometer. However, using the EEG it is possible to achieve even smaller HSVs than with whole-head planar gradiometer MEG devices. The micro-superconducting quantum interference device (SQUID) MEG device does have HSVs comparable to those of the EEG. The sensitivity distribution of planar gradiometers, however, closely resembles that of dipolar EEG leads and, therefore, the MEG and EEG record the electric activity of the brain in a very similar way.

Sensitivity distributions of EEG and MEG measurements

The resistivity values of the different tissues of the head affect the lead fields of electroencephalography (EEG). When the head is modeled with a concentric spherical model, the different resistivity values have no effect on the lead fields of the magnetoencephalography (MEG). Recent publications indicate that the resistivity of the skull is much lower than what was estimated by Rush and Driscoll. At the moment, this information on skull resistivity is, however, slightly controversial. We have compared the spatial resolution of EEG and MEG for cortical sources by calculating the half-sensitivity volumes (HSVs) of EEG and MEG as a function of electrode and magnetometer distance, respectively, with the relative skull resistivity as a parameter. Because the spatial resolution is related to the HSV, these data give an overview of the effect of these parameters on the spatial resolution of both techniques. Our calculations show that, with the new information on the resistivity of the skull, in the spherical model for cortical sources the spatial resolution of the EEG is better than that of the MEG.

/pdf/effect-of-skull-resistivity-on-the-spatial-resolutions-of-rw8jjqpf19.pdf

Effect of skull resistivity on the spatial resolutions of EEG and MEG

A computer model has been designed for the calculation of the electrical fields in the head, based on the finite difference method. This method has not previous& been applied for head modelling. The model was validated by using three concentric spheres and comparing it with an analytic model. Three levels of accuracy were tested. The forward solutions show that the finite difference algorithm work correct& and, by selecting the size of the volume elements properly, accurate results are obtained. The model will be applied to accurate and realistic geometries of the human head obtained from magnetic resonance images.

Validation of a detailed computer model for the electric fields in the brain

Correction to "Sensitivity Distributions Of EEG And MEG Measurements"

Transcranial electrical stimulation can be used for clinical investigations of the central nervous system and for monitoring of motor nerve tracts during surgical operations. We wished to reduce the pain involved with the transcranial electrical stimulation and to improve the usefulness of the method for monitoring during surgical operations. A dedicated transcranial electrical stimulator was designed having special features to reduce the pain sensation and the nerve blocking effect of anaesthetics. It provides constant current and constant voltage stimulation pulses with very short duration and high amplitude. The pulse length is adjustable in the range of 15 to 125 microseconds, while the maximum amplitude is 100 V and 1 A for voltage and current stimulation modes, respectively. Special features included high-repetition-rate pulse trains (50-2000 pulses s-1) and a three-electrode stimulation configuration. We suggest that the electrical transcranial stimulation has the potential to be a relatively painless method for routine clinical investigations and a reliable method for monitoring during surgery.

V. Suihko

Papers

Sensitivity distributions of EEG and MEG measurements

Effect of skull resistivity on the spatial resolutions of EEG and MEG

Validation of a detailed computer model for the electric fields in the brain

Correction to "Sensitivity Distributions Of EEG And MEG Measurements"

Transcranial electrical stimulator producing high amplitude pulses and pulse trains.