Using a motion capture system for spatial localization of EEG electrodes

Reis, Pedro; Lochmann, Matthias

doi:10.3389/fnins.2015.00130

Cited by 20 publications

(24 citation statements)

References 14 publications

(23 reference statements)

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“…For researchers interested in investing in having a digitizing system with the highest reliability, our results suggest that the motion capture method is currently the best option. In a previous study, Reis and Lochmann developed an activeelectrode motion capture approach for an EEG system with 30 electrodes and reported small deviation of the digitized locations from the ground truth locations [31]. In addition to having sub-millimeter variability, the motion capture method only required 1-2 seconds to digitize, assuming that the markers were already placed on the EEG electrodes.…”

Section: Discussionmentioning

confidence: 99%

“…Recent efforts have focused on developing technologies to make digitization more accessible and convenient, mainly by incorporating image-based technologies [28], [29]. For example, using photogrammetry and motion capture methods for digitization can provide accurate electrode locations in a short period of time [30], [31]. Photogrammetry involves using cameras to take a series of color images at different view angles.…”

Section: Introductionmentioning

confidence: 99%

“…Motion capture typically uses multiple infrared cameras around the capture volume to take simultaneous images to identify the locations of reflective or emitting markers. If markers are placed directly on the EEG electrodes, a motion capture system could conveniently record the position of all of the electrodes at once [26], [31]. Motion capture could also be used to record the position of the tip of a probe, a rigid body with multiple markers, to digitize 3D locations of any point in the capture volume.…”

Section: Introductionmentioning

confidence: 99%

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More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, But Current Methods Already Accurately Identify Brodmann Areas

Shirazi

Huang

2019

Preprint

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Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations. Here, we compared the reliability of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ∼ 1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was > 80%. Using a template of electrode locations reduced the Brodmann area accuracy to ∼ 50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, But Current Methods Already Accurately Identify Brodmann Areas

Shirazi

Huang

2019

Preprint

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“…Recently proposed photogrammetry methods also rely on tightly restricted movements of the subject and allow only a specific movement trajectory of the camera around the subject. 10,11 These methods require a lengthy recording of the participant (typically longer than two minutes, e.g., Delscan's Artec or GeoScan 12 ) or impose motionless requirements in an extremely specific environment (e.g., Philips' GPS). Faster, more developmentally friendly, coregistration methods are based on manual photogrammetry.…”

Section: Introductionmentioning

confidence: 99%

Video-based motion-resilient reconstruction of three-dimensional position for functional near-infrared spectroscopy and electroencephalography head mounted probes

et al. 2020

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Significance: We propose a video-based, motion-resilient, and fast method for estimating the position of optodes on the scalp. Aim: Measuring the exact placement of probes (e.g., electrodes and optodes) on a participant's head is a notoriously difficult step in acquiring neuroimaging data from methods that rely on scalp recordings (e.g., electroencephalography and functional near-infrared spectroscopy) and is particularly difficult for any clinical or developmental population. Existing methods of head measurements require the participant to remain still for a lengthy period of time, are laborious, and require extensive training. Therefore, a fast and motion-resilient method is required for estimating the scalp location of probes. Approach: We propose an innovative video-based method for estimating the probes' positions relative to the participant's head, which is fast, motion-resilient, and automatic. Our method builds on capitalizing the advantages and understanding the limitations of cutting-edge computer vision and machine learning tools. We validate our method on 10 adult subjects and provide proof of feasibility with infant subjects. Results: We show that our method is both reliable and valid compared to existing state-of-the-art methods by estimating probe positions in a single measurement and by tracking their translation and consistency across sessions. Finally, we show that our automatic method is able to estimate the position of probes on an infant head without lengthy offline procedures, a task that has been considered challenging until now. Conclusions: Our proposed method allows, for the first time, the use of automated spatial coregistration methods on developmental and clinical populations, where lengthy, motion-sensitive measurement methods routinely fail.

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“…Various methods are available for digitizing, or measuring the 3D locations, of EEG electrodes on the head [ 9 – 16 ]. Hardware digitization solutions involve some method of 3D spatial measurements, such as pointing a tracked stylus at each electrode or photogrammetry-based methods that use images taken from multiple angles to reconstruct electrode location [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

EEG electrode digitization with commercial virtual reality hardware

Cline

Coogan

2018

PLoS ONE

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Accurate spatial co-registration of EEG electrode positions with individual head models is an important component for EEG source localization and imaging. Due to variations in head shape between individuals, this requires measurements of electrode locations in each individual. Existing hardware for digitization can be accurate, but also relatively expensive. With the goal of making digitization more accessible for a range of research laboratories, we have developed an open-source software tool that can make use of less expensive consumer virtual reality hardware for EEG electrode digitization. Here we describe our developed VRDigitizer system and compare it to existing digitization solutions. Experimental evaluations were performed in a phantom head model and in 12 human subjects. In our comparison experiments, VRDigitizer was able to measure electrode positions with a mean error of 3.74 mm, compared to 1.73 mm and 2.98 mm for the commercial systems tested.

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Using a motion capture system for spatial localization of EEG electrodes

Cited by 20 publications

References 14 publications

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, But Current Methods Already Accurately Identify Brodmann Areas

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, But Current Methods Already Accurately Identify Brodmann Areas

Video-based motion-resilient reconstruction of three-dimensional position for functional near-infrared spectroscopy and electroencephalography head mounted probes

EEG electrode digitization with commercial virtual reality hardware

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