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      Faster and improved 3-D head digitization in MEG using Kinect

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          Abstract

          Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

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          Most cited references16

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          Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

          Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.
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            Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents.

            D. Cohen (1968)
            Weak alternating magnetic fields outside the human scalp, produced by alpha-rhythm currents, are demonstrated. Subject ard magnetic detector were housed in a multilayer magnetically shielded chamber. Background magnetic noise was reduced by signal-averaging. The fields near the scalp are about 1 x 10(-9) gauss (peak to peak). A course distribution shows left-right symmetry for the particular averaging technique used here.
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              Suppression of interference and artifacts by the Signal Space Separation Method.

              Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                28 October 2014
                2014
                : 8
                : 326
                Affiliations
                [1] 1Biomedical Translational Imaging Centre (BIOTIC), IWK Health Centre Halifax, NS, Canada
                [2] 2Department of Diagnostic Radiology, Dalhousie University Halifax, NS, Canada
                [3] 3Faculty of Computer Science, Dalhousie University Halifax, NS, Canada
                Author notes

                Edited by: Yaroslav O. Halchenko, Dartmouth College, USA

                Reviewed by: Sergey M. Plis, The Mind Research Network, USA; Théodore Papadopoulo, Institut National de Recherche en Informatique et en Automatique, France; Matteo Visconti Di Oleggio Castello, Dartmouth College, USA

                *Correspondence: Timothy Bardouille, IWK Health Centre, 5850 – 5980 University Ave., 1st floor Goldbloom pavilion, Halifax, NS B3K 6R8, Canada e-mail: tim.bardouille@ 123456dal.ca

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience.

                Article
                10.3389/fnins.2014.00326
                4211394
                25389382
                114377b0-9ba1-4cce-9b8b-66af12d848f5
                Copyright © 2014 Vema Krishna Murthy, MacLellan, Beyea and Bardouille.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 July 2014
                : 26 September 2014
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 19, Pages: 7, Words: 5327
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                magnetoencephalography (meg),head position indicator (hpi),laser scanner,microsoft kinect,color recognition,alignment,localization

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