Electro- and Magnetoencephalographic Measurements

Knösche, Thomas R.; Haueisen, Jens

doi:10.1007/978-3-030-74918-7_3

Cited by 3 publications

(4 citation statements)

References 96 publications

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“…Therefore, we were not able to perform anatomical or functional MRI of the brain. Magnetoencephalography (MEG) is less sensitive to anatomical imprecisions for source reconstruction compared to electroencephalography (EEG) 44 . Therefore, we decided to use MEG to map the activity correlated to the limb motor intentions.…”

Section: Pre-operative Magnetoencephalographymentioning

confidence: 99%

Walking naturally after spinal cord injury using a brain–spine interface

Lorach,

Galvez,

Spagnolo

et al. 2023

Nature

145

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A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis1,2. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain–spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals3 and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking4–6. A highly reliable BSI is calibrated within a few minutes. This reliability has remained stable over one year, including during independent use at home. The participant reports that the BSI enables natural control over the movements of his legs to stand, walk, climb stairs and even traverse complex terrains. Moreover, neurorehabilitation supported by the BSI improved neurological recovery. The participant regained the ability to walk with crutches overground even when the BSI was switched off. This digital bridge establishes a framework to restore natural control of movement after paralysis.

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Section: Pre-operative Magnetoencephalographymentioning

confidence: 99%

Walking naturally after spinal cord injury using a brain–spine interface

Lorach,

Galvez,

Spagnolo

et al. 2023

Nature

145

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“…Inverse problem. The Eq (3) poses an ill-posed inverse problem with respect to the unknown activity x, and the source currents corresponding to the entries of x are assumed to be dipolar [1]. Since the non-unique solution of this problem is subject to the applied inverse approach, we reconstruct x use three different techniques, MNE, sLORETA, and the dipole scan method, which are briefly reviewed below.…”

Section: Eeg Forward Modelmentioning

confidence: 99%

“…Non-invasive electroencephalography (EEG) source localization [ 1 ] is the process of identifying active regions of the brain through a set of measurements. This is accomplished by solving an inverse problem [ 2 ], where the measured EEG signals constitute the data and the distribution of electric brain activity the unknown.…”

Section: Introductionmentioning

confidence: 99%

Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures

Galaz Prieto,

Lahtinen,

Samavaki

et al. 2023

PLoS ONE

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This paper introduces an automated approach for generating a finite element (FE) discretization of a multi-compartment human head model for electroencephalographic (EEG) source localization. We aim to provide an adaptable FE mesh generation tool for EEG studies. Our technique relies on recursive solid angle labeling of a surface segmentation coupled with smoothing, refinement, inflation, and optimization procedures to enhance the mesh quality. In this study, we performed numerical meshing experiments with the three-layer Ary sphere and a magnetic resonance imaging (MRI)-based multi-compartment head segmentation which incorporates a comprehensive set of subcortical brain structures. These experiments are motivated, on one hand, by the sensitivity of non-invasive subcortical source localization to modeling errors and, on the other hand, by the present lack of open EEG software pipelines to discretize all these structures. Our approach was found to successfully produce an unstructured and boundary-fitted tetrahedral mesh with a sub-one-millimeter fitting error, providing the desired accuracy for the three-dimensional anatomical details, EEG lead field matrix, and source localization. The mesh generator applied in this study has been implemented in the open MATLAB-based Zeffiro Interface toolbox for forward and inverse processing in EEG and it allows for graphics processing unit acceleration.

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“…A huge advantage of EEG over, e.g., functional magnetic resonance imaging (fMRI), is its time resolution in the millisecond range. To localize the brain activity underlying a measured signal it is necessary to solve the EEG inverse problem (Knösche and Haueisen, 2022 ). As a prerequisite for solving the EEG inverse problem, it is necessary to model the propagation of the electric fields evoked by brain activity through the head tissues, which are measured as the EEG signal at the head surface (EEG forward problem).…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity of EEG source analysis to tissue conductivity uncertainties

Vorwerk,

Wolters,

Baumgarten

2024

Front. Hum. Neurosci.

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IntroductionTo reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial—but often neglected—aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface.MethodsWe employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions.ResultsFor sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity.DiscussionOur results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.

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Electro- and Magnetoencephalographic Measurements

Cited by 3 publications

References 96 publications

Walking naturally after spinal cord injury using a brain–spine interface

Walking naturally after spinal cord injury using a brain–spine interface

Multi-compartment head modeling in EEG: Unstructured boundary-fitted tetra meshing with subcortical structures

Global sensitivity of EEG source analysis to tissue conductivity uncertainties

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