IEEE J Biomed Health Inform. 2016 Mar;20(2):539-48. doi: 10.1109/JBHI.2015.2392785. Epub 2015 Jan 19.
Head movements during an MEG recording are commonly considered an obstacle. In this computer simulation study, we introduce an approach, the virtual MEG helmet (VMH), which employs the head movements for data quality improvement. With a VMH, a denser MEG helmet is constructed by adding new sensors corresponding to different head positions. Based on the Shannon's theory of communication, we calculated the total information as a figure of merit for comparing the actual 306-sensor Elekta Neuromag helmet to several types of the VMH. As source models, we used simulated randomly distributed source current (RDSC), simulated auditory and somatosensory evoked fields. Using the RDSC model with the simulation of 360 recorded events, the total information (bits/sample) was 989 for the most informative single head position and up to 1272 for the VMH (addition of 28.6%). Using simulated AEFs, the additional contribution of a VMH was 12.6% and using simulated SEF only 1.1%. For the distributed and bilateral sources, a VMH can provide a more informative sampling of the neuromagnetic field during the same recording time than measuring the MEG from one head position. VMH can, in some situations, improve source localization of the neuromagnetic fields related to the normal and pathological brain activity. This should be investigated further employing real MEG recordings.
在 MEG 记录期间，头部运动会被普遍认为是一个障碍。在这项计算机模拟研究中，我们引入了一种方法，即虚拟 MEG 头盔（VMH），它利用头部运动来提高数据质量。使用 VMH，可以通过向不同的头部位置添加新的传感器来构建更密集的 MEG 头盔。基于 Shannon 的通信理论，我们计算了总信息量，作为比较实际的 306 个传感器 Elekta Neuromag 头盔和几种类型的 VMH 的优劣的指标。作为源模型，我们使用模拟的随机分布源电流（RDSC）、模拟的听觉和躯体感觉诱发电场。使用具有 360 个记录事件模拟的 RDSC 模型，信息量（比特/样本）对于最具信息量的单个头部位置为 989，对于 VMH 则高达 1272（增加了 28.6%）。使用模拟的 AEF，VMH 的额外贡献为 12.6%，而仅使用模拟的 SEF 则为 1.1%。对于分布式和双侧源，与从一个头部位置测量 MEG 相比，VMH 可以在相同的记录时间内提供对神经磁场的更具信息量的采样。在某些情况下，VMH 可以改善与正常和病理大脑活动相关的神经磁场的源定位。这应该通过使用真实的 MEG 记录进一步研究。
