A simplex method for the calibration of a MEG device

IF 0.3 Q4 MATHEMATICS

Communications in Applied and Industrial Mathematics Pub Date : 2019-01-01 DOI:10.2478/caim-2019-0005

V. Vivaldi, Sara Sommariva, A. Sorrentino

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引用次数: 2

Abstract

Abstract MagnetoEncephaloGraphy (MEG) devices are helmet–shaped arrays of sensors that measure the tiny magnetic fields produced by neural currents. As they operate at low temperature, they are typically immersed in liquid helium. However, during the cooling process the sensor position and shape can change, with respect to nominal values, due to thermal stress. This implies that an accurate sensor calibration is required before a MEG device is utilized in either neuroscientific research or clinical workflow. Here we describe a calibration scheme developed for the optimal use of a MEG system recently realized at the “Istituto di Cibernetica e Biofisica” of the Italian CNR. To achieve the calibration goal a dedicated magnetic source is used (calibration device) and the geometric parameters of the sensors are determined through an optimisation procedure, based on the Nelder-Mead algorithm, which maximises the correlation coefficient between the predicted and the recorded magnetic field. Then the sensitivity of the sensors is analytically estimated. The developed calibration procedure is validated with synthetic data mimicking a real scenario.

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一种单纯形法校正磁磁图装置

脑磁图(MEG)设备是一种头盔形状的传感器阵列，用于测量由神经电流产生的微小磁场。当它们在低温下工作时，它们通常浸泡在液氦中。然而，在冷却过程中，传感器的位置和形状可以改变，相对于标称值，由于热应力。这意味着在神经科学研究或临床工作流程中使用MEG设备之前，需要精确的传感器校准。在这里，我们描述了一种校准方案，该方案是为了优化使用最近在意大利CNR的“生物生物学研究所”实现的MEG系统而开发的。为了实现校准目标，使用专用磁源(校准装置)，并通过基于Nelder-Mead算法的优化程序确定传感器的几何参数，该算法使预测和记录的磁场之间的相关系数最大化。然后对传感器的灵敏度进行了分析估计。用模拟真实场景的合成数据验证了所开发的校准程序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Communications in Applied and Industrial Mathematics Mathematics-Applied Mathematics

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

16 weeks

期刊介绍： Communications in Applied and Industrial Mathematics (CAIM) is one of the official journals of the Italian Society for Applied and Industrial Mathematics (SIMAI). Providing immediate open access to original, unpublished high quality contributions, CAIM is devoted to timely report on ongoing original research work, new interdisciplinary subjects, and new developments. The journal focuses on the applications of mathematics to the solution of problems in industry, technology, environment, cultural heritage, and natural sciences, with a special emphasis on new and interesting mathematical ideas relevant to these fields of application . Encouraging novel cross-disciplinary approaches to mathematical research, CAIM aims to provide an ideal platform for scientists who cooperate in different fields including pure and applied mathematics, computer science, engineering, physics, chemistry, biology, medicine and to link scientist with professionals active in industry, research centres, academia or in the public sector. Coverage includes research articles describing new analytical or numerical methods, descriptions of modelling approaches, simulations for more accurate predictions or experimental observations of complex phenomena, verification/validation of numerical and experimental methods; invited or submitted reviews and perspectives concerning mathematical techniques in relation to applications, and and fields in which new problems have arisen for which mathematical models and techniques are not yet available.