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 [Cornell University]
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
D33sama发布了新的文献求助10
刚刚
Janely完成签到,获得积分10
1秒前
老的火龙果的应助被西渡朝朝采纳,获得10
2秒前
隐形曼青的应助被nana采纳,获得10
3秒前
桐桐的应助被longfei采纳,获得10
3秒前
传奇3的应助被优美的冷梅采纳,获得10
3秒前
5秒前
6秒前
赘婿的应助被咚咚采纳,获得10
7秒前
王欣发布了新的文献求助10
8秒前
LUVI完成签到,获得积分10
8秒前
健康的悲完成签到,获得积分20
8秒前
MeE关注了科研通微信公众号
9秒前
9秒前
10秒前
认真芷容的应助被逝水无痕采纳,获得10
10秒前
尔舟行完成签到 ,获得积分10
10秒前
10秒前
10秒前
尼古拉斯发布了新的文献求助10
11秒前
小尾巴完成签到 ,获得积分10
11秒前
我球呢完成签到,获得积分10
12秒前
13秒前
ZZX发布了新的文献求助30
14秒前
nana发布了新的文献求助10
14秒前
jiumi发布了新的文献求助10
15秒前
小鱼完成签到,获得积分10
15秒前
16秒前
汉堡包的应助被Me采纳,获得10
16秒前
温特完成签到 ,获得积分10
17秒前
19秒前
胡月月发布了新的文献求助10
19秒前
时鹏飞发布了新的文献求助10
20秒前
20秒前
20秒前
zeus发布了新的文献求助10
22秒前
nana完成签到,获得积分10
23秒前
23秒前
longfei发布了新的文献求助10
25秒前
Owen的应助被防风邶采纳,获得10
25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
自動車の空力技術 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Issues in Task-Based Language Teaching 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 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7783756
求助须知:如何正确求助?哪些是违规求助? 9323034
关于积分的说明 20392824
捐赠科研通 7372379
什么是DOI,文献DOI怎么找? 3320767
关于科研通互助平台的介绍 2468765
邀请新用户注册赠送积分活动 2336986