Determining sensor geometry and gain in a wearable MEG system

可穿戴计算机 几何学 计算机科学 物理 数学 嵌入式系统
作者
Ryan M. Hill,G. Rivero,Ashley J. Tyler,Holly Schofield,Cody Doyle,James Osborne,David Bobela,Lukas Rier,J. M. Gibson,Zoe Tanner,Elena Boto,Richard Bowtell,Matthew J. Brookes,Vishal Shah,Niall Holmes
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2410.08718
摘要

Optically pumped magnetometers (OPMs) are compact and lightweight sensors that can measure magnetic fields generated by current flow in neuronal assemblies in the brain. Such sensors enable construction of magnetoencephalography (MEG) instrumentation, with significant advantages over conventional MEG devices including adaptability to head size, enhanced movement tolerance, lower complexity and improved data quality. However, realising the potential of OPMs depends on our ability to perform system calibration, which means finding sensor locations, orientations, and the relationship between the sensor output and magnetic field (termed sensor gain). Such calibration is complex in OPMMEG since, for example, OPM placement can change from subject to subject (unlike in conventional MEG where sensor locations or orientations are fixed). Here, we present two methods for calibration, both based on generating well-characterised magnetic fields across a sensor array. Our first device (the HALO) is a head mounted system that generates dipole like fields from a set of coils. Our second (the matrix coil (MC)) generates fields using coils embedded in the walls of a magnetically shielded room. Our results show that both methods offer an accurate means to calibrate an OPM array (e.g. sensor locations within 2 mm of the ground truth) and that the calibrations produced by the two methods agree strongly with each other. When applied to data from human MEG experiments, both methods offer improved signal to noise ratio after beamforming suggesting that they give calibration parameters closer to the ground truth than factory settings and presumed physical sensor coordinates and orientations. Both techniques are practical and easy to integrate into real world MEG applications. This advances the field significantly closer to the routine use of OPMs for MEG recording.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
热烈完成签到 ,获得积分10
2秒前
MR完成签到 ,获得积分10
3秒前
我不是哪吒完成签到 ,获得积分10
4秒前
自由的云朵完成签到 ,获得积分10
4秒前
Freddie发布了新的文献求助10
5秒前
hyw完成签到,获得积分10
6秒前
星辰大海应助BENRONG采纳,获得10
9秒前
认真发夹完成签到 ,获得积分10
9秒前
10秒前
Freddie发布了新的文献求助200
12秒前
eryu25完成签到 ,获得积分10
12秒前
12秒前
14秒前
传奇3应助自然冥茗采纳,获得10
14秒前
沭阳检验医师完成签到,获得积分0
14秒前
16秒前
默存完成签到,获得积分0
17秒前
Freddie发布了新的文献求助10
18秒前
tiptip应助既望采纳,获得10
20秒前
20秒前
BENRONG发布了新的文献求助10
21秒前
wanci应助JarryChao采纳,获得10
21秒前
坚定的小蘑菇完成签到 ,获得积分10
23秒前
Freddie发布了新的文献求助10
24秒前
英姑应助贪玩的秋寒采纳,获得10
25秒前
论文写写写写到厌倦完成签到,获得积分10
26秒前
26秒前
文心完成签到 ,获得积分10
27秒前
搜集达人应助Nemo采纳,获得10
27秒前
Haoyu完成签到,获得积分10
30秒前
baimo完成签到,获得积分10
31秒前
31秒前
Freddie发布了新的文献求助10
32秒前
ws完成签到,获得积分10
32秒前
学习发布了新的文献求助30
32秒前
下小雨完成签到 ,获得积分10
33秒前
33秒前
演员完成签到,获得积分10
33秒前
34秒前
Haoyu发布了新的文献求助10
36秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6348564
求助须知:如何正确求助?哪些是违规求助? 8163566
关于积分的说明 17174591
捐赠科研通 5405041
什么是DOI,文献DOI怎么找? 2861881
邀请新用户注册赠送积分活动 1839643
关于科研通互助平台的介绍 1688947