Application of polymer sensitive MRI sequence to localization of EEG electrodes

Butler, Russell; Gilbert, Guillaume; Descoteaux, Maxime; Bernier, Pierre‐Michel; Whittingstall, Kevin

doi:10.1016/j.jneumeth.2016.12.013

Cited by 12 publications

(15 citation statements)

References 15 publications

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“…A total of 5 runs were performed. After the EEG acquisition, the subject was returned to the MR scanner to acquire an ultra-short echo time MR sequence used to locate the EEG electrodes as described by Butler et al (2017). Approval for this study was obtained from the Centre de Sante et de Services Sociaux-Institut Universitaire de Geriatrie de Sherbrooke and Centre Hospitalier Universitaire de Sherbrooke ethics committees.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

White matter information flow mapping from diffusion MRI and EEG

et al. 2019

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The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.

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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

White matter information flow mapping from diffusion MRI and EEG

et al. 2019

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“…The electroencephalograph (EEG) technology is now widely used in clinical medicine such as epilepsy, coma, brain deaths and so on, due to its use, economy, safety, and non-invasive detection (Jeon et al, 2018). To well-use the EEG technology for analyzing the brain activities, it is important to accurately locate the position of scalp signal in the cerebral cortex (Qian and Sheng, 2011; Reis and Lochmann, 2015; Butler et al, 2016; Saha et al, 2017; Liu et al, 2018). At present, there are several kinds of EEG electrode localization methods, including (1) manual method, (2) digital radio frequency (RF) electromagnetic instrument, (3) magnetic resonance (MR), (4) ultrasonic transmission and reflection, and (5) photogrammetric method (Koessler et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Spatial Localization of EEG Electrodes in a TOF+CCD Camera System

Chen

Hui-li

et al. 2019

Front. Neuroinform.

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A crucial link of electroencephalograph (EEG) technology is the accurate estimation of EEG electrode positions on a specific human head, which is very useful for precise analysis of brain functions. Photogrammetry has become an effective method in this field. This study aims to propose a more reliable and efficient method which can acquire 3D information conveniently and locate the source signal accurately in real-time. The main objective is identification and 3D location of EEG electrode positions using a system consisting of CCD cameras and Time-of-Flight (TOF) cameras. To calibrate the camera group accurately, differently to the previous camera calibration approaches, a method is introduced in this report which uses the point cloud directly rather than the depth image. Experimental results indicate that the typical distance error of reconstruction in this study is 3.26 mm for real-time applications, which is much better than the widely used electromagnetic method in clinical medicine. The accuracy can be further improved to a great extent by using a high-resolution camera.

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“…Instead of selecting the center of each electrode in a 3D image, we choose to use a more convenient procedure for the manual detection. Following Butler et al [2017], the manual detection was performed by picking up the Cartesian position (x i , y i , z i ) of each 64 electrodes for each subject on a pancake view, which is roughly a 2D projection of the scalp (de Munck et al [2012]).…”

Section: Validation Of the Methodsmentioning

confidence: 99%

“…Compared to other existing approaches, the proposed method does not need additional hardware (like 3D electromagnetic digitizer devices (Adjamian et al [2004]; Whalen et al [2008]), artificial electrode markers (Sijbers et al [2000]) or laser scanner (Koessler et al [2011]; Bardouille et al [2012])), which might be uncomfortable for the subject if he must stay still during acquisition (Le et al [1998]) and add time to the preparation of the patient. Semi-automated electrodes localisation methods exist (de Munck et al [2012]; Butler et al [2017]), which require a manual fiducial landmark identification to guide co-registration without any markers but these approach relies on the efficiency of the accuracy of the operator. Another automated method was recently developed and shown great results with an anatomical MR image (Marino et al [2016]), however, this method is only working with a high density cap also compatible with MRI: the GES 300 from Geodesic EEG Systems.…”

Section: Introductionmentioning

confidence: 99%

“…It allows to visualise the tissues with a very short T2 and T2 * , such as cortical bone, tendons and ligaments (Holmes and Bydder [2005], Keereman et al [2010]). This sequence is all the more interesting in our context because it enables the visualisation of the MR compatible electrodes (Butler et al [2017], Springer et al [2008]) on the scalp with a capability to be performed rapidly enough to not overwhelm the whole MRI protocol.…”

Section: Introductionmentioning

confidence: 99%

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Automated Electrodes Detection during simultaneous EEG/fMRI

Fleury

Barillot

Mano

et al. 2018

Preprint

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The coupling of Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) enables the measure of brain activity at high spatial and temporal resolution. The localisation of EEG sources depends on several parameters including the knowledge of the position of the electrodes on the scalp. An accurate knowledge about this information is important for source reconstruction. Currently, when acquiring EEG and fMRI together, the position of the electrodes is generally estimated according to fiducial points by using a template. In the context of simultaneous EEG/fMRI acquisition, a natural idea is to use magnetic resonance (MR) images to localise EEG electrodes. However, most MR compatible electrodes are built to be almost invisible on MR Images. Taking advantage of a recently proposed Ultra short Echo Time (UTE) sequence, we introduce a fully automatic method to detect and label those electrodes in MR images. Our method was tested on 8 subjects wearing a 64-channel EEG cap. This automated method showed an average detection accuracy of 94% and the average position error was 3.1 mm. These results suggest that the proposed method has potential for determining the position of the electrodes during simultaneous EEG/fMRI acquisition with a very light cost procedure.

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Application of polymer sensitive MRI sequence to localization of EEG electrodes

Cited by 12 publications

References 15 publications

White matter information flow mapping from diffusion MRI and EEG

White matter information flow mapping from diffusion MRI and EEG

Spatial Localization of EEG Electrodes in a TOF+CCD Camera System

Automated Electrodes Detection during simultaneous EEG/fMRI

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