workshop / hangzhou2026 / analysis /

Analysis of OPM-MEG data

Introduction

Traditional MEG systems use superconducting quantum interference devices (SQUIDs), which require expensive cryogenic cooling with liquid helium and involve placing the sensors 1.5-2 cm from the scalp. Optically pumped magnetometers (OPMs), a new generation of MEG, eliminate the need for cryogenic cooling, allowing sensors to be placed closer to the scalp. OPMs are small individual sensors that allow the researcher to choose where to place them over the head. These sensors are held in place by 3D-printed helmets, which can be customized for each subject.

Even though both the SQUID and the OPM systems detect the brainā€™s magnetic fields, their data analysis steps differ a bit. In this tutorial we explore the differences in preprocessing and coregistration between the two systems.

OPMs are flexible in their placement, allowing for new recording strategies. These new recording strategies lead to new data analysis strategies. For example, in this tutorial we use a small number of OPMs (32 sensors) and do sequential recordings in which we position them over different places over the scalp.

OPMs are magnetometers, as their name suggests. Magnetometers are more sensitive to environmental noise than gradiometers, which most SQUID systems have. Several data analysis algorithms to remove environmental noise have been proposed (see Seymour et al (2022) for more details). In this tutorial, we apply homogeneous field correction (HFC). HFC works better with a large number of sensors.

Unlike SQUID systems, which have standard coregistration strategies, OPMs donā€™t have a single coregistration standard. In this tutorial, we coregister the OPMs with the MRI using an optical 3D scanner which captures the participantā€™s facial features along with the OPM helmet (Zetter et al., 2019).

This tutorial combines the FieldTrip tutorials on preprocessing of OPM data and coregistration of OPM data. It does not cover follow-up analyses (like source reconstruction) which in principle should not differ from the SQUID follow-up analyses, or alternative coregistration methods, which are covered in the tutorial on coregistration of OPM data.

This hands-on session is the follow-up of the hands-on session on planning the acquisition of OPM-MEG data, in which we designed an OPM helmet, placed OPM sensors according to the extended 10-20 system, and computed the sensitivity of the resulting sensor array. In this session we analyze OPM-MEG data that was recorded with a different helmet design, namely the FieldLine smart helmet.

Background

In this tutorial we will use recordings made with 32 OPM sensors placed in an adult-sized ā€œsmartā€ helmet with a total of 144 slots. This helmet is called ā€œsmartā€ as each slot allows the sensor to slide in until it touches the head surface, regardless of the head size and shape. To limit head movements we mounted the helmet on a wooden plate located behind the subject chair.

To acquire a measurement for each of the 144 helmet slots, we divided the experiment into six runs. To maintain the participantā€™s head fixed between runs, we kept 9 sensors around the participantā€™s head fixed for all the runs. The remaining 23 sensors were moved to different helmet slots in each run to cover the whole scalp as homogeneously as possible.

The dataset used in this tutorial

The data for this tutorial was recorded with a 32-sensor FieldLine HEDscan v3 system with a so-called smart helmet. Each OPM sensor has one channel that measures the normal component of the magnetic field.

We perform a left median nerve stimulation experiment on a single participant in both the SQUID and the OPM system. We expect to find a dipole 20 ms post-stimulation in the right primary somatosensory area (Andersen & Dalal, 2021; Buchner et al., 1994).

The dataset can be downloaded from our download server.

Setup

Install and start FieldTrip

If you have not yet done so, install FieldTrip and start it with

restoredefaultpath
cd path_to_directory/fieldtrip-xxxxxxxx
addpath(pwd)
ft_defaults

Download the data

Download the sub-01 directory from the download server, and cd into the directory that contains it. The file paths used in this tutorial are relative to your current working directory. The data is organized as follows:

sub-01/anat
sub-01/meg/opm
sub-01/meg/squid

The directory sub-01/anat contains the anatomical MRI, sub-01/meg/opm contains the six OPM recordings (in fif format) together with the optical 3D scan and the template of the FieldLine helmet, and sub-01/meg/squid contains the SQUID recording (in the form of a CTF .ds directory) together with the Polhemus head shape.

Procedure

In this tutorial for the SQUIDs we will take the following steps:

For the OPMs we will take the following steps:

Preprocessing & computing ERFs

SQUID

We begin by reading the SQUID data and defining trials. We select trials in which left median nerve stimulation occurred (trigger code = 1). In the experiment, the inter-trial interval ranged from 800-1200 ms, so it makes sense to select a 200 ms prestimulus and 400 ms poststimulus window.

cfg                     = [];
cfg.dataset             = 'sub-01/meg/squid/sub-01_ses-01_task-MedianNervesStim_squid.ds';
cfg.trialdef.eventtype  = 'UPPT001';
cfg.trialdef.eventvalue = 1;
cfg.trialdef.prestim    = 0.2;
cfg.trialdef.poststim   = 0.4;
cfg                     = ft_definetrial(cfg);

cfg.demean         = 'yes'; % apply baseline correction
cfg.baselinewindow = [-Inf 0];
cfg.channel        = 'MEG';
cfg.continuous     = 'yes';
data_squid         = ft_preprocessing(cfg);

save data_squid data_squid

The summary method in ft_rejectvisual allows you to manually remove trials and/or channels that have high variance.

load data_squid data_squid

cfg              = [];
cfg.method       = 'summary';
data_squid_clean = ft_rejectvisual(cfg, data_squid);

save data_squid_clean data_squid_clean

We start by removing trials that have higher variance (bottom-left plot). Based on our data, a reasonable threshold to choose is 8e-25 Tesla-squared. Next, we assess the channel variance (top-right plot). Removing trials with high variance resulted in reducing the channel variance too, so no need to reject any channels.

We then calculate the ERFs:

load data_squid_clean data_squid_clean

cfg       = [];
avg_squid = ft_timelockanalysis(cfg, data_squid_clean);

save avg_squid avg_squid

We plot the activity from all the sensors.

load avg_squid avg_squid

cfg = [];
cfg.layout = 'CTF275_helmet';
ft_multiplotER(cfg, avg_squid); % use interactively

Interactive mode: Explore the event-related potential by dragging boxes around (groups of) sensors and time points in the ā€˜multiplotā€™ and the resulting ā€˜singleplotsā€™ and ā€˜topoplotsā€™. Can you find the time window that the dipolar activity at the right primary somatosensory area appears?

We can also plot this dipolar pattern with ft_topoplotER.

load avg_squid avg_squid

cfg = [];
cfg.xlim = [0.035 0.050];
cfg.layout = 'CTF275_helmet';
ft_topoplotER(cfg, avg_squid);

print -dpng analysis_topo_squid.png

OPM

d = dir('sub-01/meg/opm/*.fif');

for i = 1:6 % 6 experimental runs
    cfg                     = [];
    cfg.dataset             = fullfile(d(i).folder, d(i).name);
    cfg.trialdef.eventtype  = 'ai61';
    cfg.trialdef.eventvalue = 1;
    cfg.trialdef.prestim    = 0.2;
    cfg.trialdef.poststim   = 0.4;
    cfg                     = ft_definetrial(cfg);

    cfg.demean         = 'yes'; % apply baseline correction
    cfg.baselinewindow = [-Inf 0];
    cfg.channel        = {'all', '-ai61'};
    data_opm(i)        = ft_preprocessing(cfg);
end

save data_opm data_opm

OPMs are magnetometers, which are more sensitive to the environmental noise than the SQUID gradiometers. To remove some of the environmental noise, several algorithmic denoising techniques have been proposed (Seymour et al., 2022). In this tutorial we will apply homogeneous field correction (HFC) (Tierney et al., 2021).

load data_opm data_opm

for i = 1:6
    data_opm_nohdr(i) = rmfield(data_opm(i), 'hdr'); % this is needed so that ft_denoise_hfc() works

    cfg               = [];
    data_opm_hfc(i)   = ft_denoise_hfc(cfg, data_opm_nohdr(i));
end

save data_opm_hfc data_opm_hfc

We will now manually remove trials with high variance.

load data_opm_hfc data_opm_hfc

for i = 1:6
    cfg                   = [];
    cfg.method            = 'summary';
    data_opm_hfc_clean(i) = ft_rejectvisual(cfg, data_opm_hfc(i));
end

save data_opm_hfc_clean data_opm_hfc_clean

For the SQUID-based recordings, we removed trials with variance above the threshold of 8e-25 Tesla-squared. Is it possible to use the same threshold for the OPMs? If not, why and does this affect the quality of the OPM signal? Remember that (i) OPMs are magnetometers, and (ii) they are placed closer to the scalp than the SQUIDs.

load data_opm_hfc_clean data_opm_hfc_clean

for i = 1:6
    cfg        = [];
    avg_opm(i) = ft_timelockanalysis(cfg, data_opm_hfc_clean(i));
end

save avg_opm avg_opm

Before concatenating the six runs, we need to rename duplicate channels. Duplicates arise for three reasons: (i) 9 sensors were fixed across all runs, (ii) some sensors in the sixth run were in the same slot as the fifth run due to limited remaining free slots, and (iii) errors by the researcher placing a sensor in the same slot between runs. During the recording an Excel sheet was used to keep track in which slot each sensor was placed. We use that information to rename the channels.

load avg_opm avg_opm

avg_opm_rename = avg_opm;

% 9 sensors were fixed across all runs. Note that L101 was not working, so we do not need to rename it.
for k = 2:6
    index = find(ismember(avg_opm(k).label, {'R101_bz', 'L108_bz', 'R503_bz', 'L503_bz', 'L507_bz', 'R507_bz', 'R212_bz', 'L212_bz'})); % 2 in the back, 2 right, 2 left, 1 front, 1 top
    for i = 1:length(index)
        avg_opm_rename(k).label{index(i)} = strcat(avg_opm(k).label{index(i)}, '_', num2str(k));
    end
end

% some sensors in the sixth run were in the same slot as the fifth run
for k = 6 % 6th run
    index = find(ismember(avg_opm_rename(k).label, {'R114_bz', 'L504_bz'}));
    for i = 1:length(index)
        avg_opm_rename(k).label{index(i)} = strcat(avg_opm_rename(k).label{index(i)}, '_', num2str(k));
    end
end

% researcher's errors
for k = 2 % 2nd run
    index = find(ismember(avg_opm_rename(k).label, {'L214_bz'}));
    for i = 1:length(index)
        avg_opm_rename(k).label{index(i)} = strcat(avg_opm_rename(k).label{index(i)}, '_', num2str(k));
    end
end

for k = 5 % 5th run
    index = find(ismember(avg_opm_rename(k).label, {'R504_bz', 'L504_bz'}));
    for i = 1:length(index)
        avg_opm_rename(k).label{index(i)} = strcat(avg_opm_rename(k).label{index(i)}, '_', num2str(k));
    end
end

save avg_opm_rename avg_opm_rename

We concatenate the ERFs across the six runs to simulate a single experiment with 144 sensors (note: there are some missing sensors, so in the end we do not have 144 sensors).

load avg_opm_rename avg_opm_rename

cfg           = [];
cfg.appenddim = 'chan';
append_opm    = ft_appendtimelock(cfg, avg_opm_rename(1), avg_opm_rename(2), avg_opm_rename(3), avg_opm_rename(4), avg_opm_rename(5), avg_opm_rename(6));

% rename the appended average to "avg", as this is the default in FieldTrip
append_opm.avg = append_opm.trial;
append_opm     = rmfield(append_opm, 'trial');

save append_opm append_opm

There are some missing sensors. To avoid interpolation or extrapolation artifacts in the topography, we can add NaNs for the missing sensors and then tell ft_topoplotER to exclude those using the cfg.interpolatenan = 'no' option.

load append_opm append_opm

% read the layout of the FieldLine beta2 helmet with all 144 slots
cfg = [];
cfg.layout = 'fieldlinebeta2bz_helmet';
layout = ft_prepare_layout(cfg);

missing = setdiff(layout.label, append_opm.label);

% missing =
%
%   8x1 cell-array
%
%     {'L101_bz'}
%     {'L105_bz'}
%     {'L312_bz'}
%     {'L604_bz'}
%     {'R105_bz'}
%     {'R603_bz'}
%     {'R604_bz'}
%     {'R605_bz'}

% make a copy of the original timelock structure, remove two fields that we don't care about
append_opm_nan = rmfield(append_opm, {'dof', 'var'});

% add NaNs to the extra slots from the helmet that are missing
append_opm_nan.label = cat(1, append_opm_nan.label(:), missing(:));
append_opm_nan.avg   = cat(1, append_opm_nan.avg, nan(numel(missing), numel(append_opm_nan.time)));

save append_opm_nan append_opm_nan

load append_opm_nan append_opm_nan

cfg            = [];
cfg.layout     = 'fieldlinebeta2bz_helmet';
cfg.showlabels = 'yes';
cfg.ylim       = 'maxabs';
ft_multiplotER(cfg, append_opm_nan);

load append_opm_nan append_opm_nan

cfg                = [];
cfg.xlim           = [0.035 0.050];
cfg.layout         = 'fieldlinebeta2bz_helmet';
cfg.marker         = 'labels';
cfg.markerfontsize = 4;
cfg.zlim           = 'maxabs';
cfg.interpolatenan = 'no';
ft_topoplotER(cfg, append_opm_nan);

print -dpng analysis_topo_opm.png

Plot the topography including NaNs, then compare it with the topography where you excluded NaNs.

Coregistration

SQUID

We are now going to coregister the anatomical MRI to the SQUID coordinate system. We read the MRI, the SQUID channels and the Polhemus head shape in memory. We recommend converting all quantities to SI units to ensure consistent units throughout your pipeline.

mri    = ft_read_mri('sub-01/anat/sub-01_acq-mprage_T1w.nii');
ctf275 = ft_read_sens('sub-01/meg/squid/sub-01_ses-01_task-MedianNervesStim_squid.ds', 'senstype', 'meg');
shape  = ft_read_headshape('sub-01/meg/squid/sub-01_ses-01_headshape.pos');

mri    = ft_convert_units(mri, 'm');
ctf275 = ft_convert_units(ctf275, 'm');
shape  = ft_convert_units(shape, 'm');

save mri mri
save ctf275 ctf275
save shape shape

To understand coregistration, you first need to know what coordinate systems are. Coregistration is about aligning data that is initially expressed in different coordinate systems into a single coordinate system.

Start by plotting the MRI, the SQUID sensors, and the Polhemus head shape to see in which coordinate systems these are expressed. You can use ft_determine_coordsys for that.

We coregister the MRI to the SQUIDs by converting the MRI coordinate system to match that of the SQUIDs. In other words, we ensure that the three fiducials (nasion, LPA and RPA) defined in the MRI coordinate system become aligned to the same three fiducials defined in the SQUID coordinate system.

load shape shape
load ctf275 ctf275

% assign the 'ctf' coordinate system to the MRI
cfg              = [];
cfg.method       = 'interactive';
cfg.coordsys     = 'ctf'; % ALS coordinate system
mri_realigned1   = ft_volumerealign(cfg, mri);

save mri_realigned1 mri_realigned1

To refine the coregistration, we can also coregister the Polhemus head shape with the skin surface that is extracted from the previously aligned MRI. Now not only the fiducials but also other skin surface points acquired with the Polhemus are used for the alignment.

load mri_realigned1 mri_realigned1

cfg = [];
cfg.method = 'headshape';
cfg.headshape.headshape = shape;
mri_realigned2 = ft_volumerealign(cfg, mri_realigned1);

% check
cfg = [];
cfg.method = 'headshape';
cfg.headshape.headshape = shape;
cfg.headshape.icp = 'no';
ft_volumerealign(cfg, mri_realigned2);

save mri_realigned2 mri_realigned2

Letā€™s plot the sensor topography in 3D using ft_plot_topo3d. This will help us visualize where the sensors are positioned relative to the head. To make this clearer, weā€™ll also display the scalp and brain surfaces.

First, weā€™ll prepare the surface mesh of the scalp and brain:

load mri_realigned2 mri_realigned2

cfg                = [];
cfg.output         = {'brain', 'scalp'};
mri_segmented      = ft_volumesegment(cfg, mri_realigned2);

cfg                = [];
cfg.tissue         = {'brain'};
mesh_brain         = ft_prepare_mesh(cfg, mri_segmented);

cfg                = [];
cfg.tissue         = {'scalp'};
mesh_scalp         = ft_prepare_mesh(cfg, mri_segmented);

save mesh_brain mesh_brain
save mesh_scalp mesh_scalp

Now, weā€™ll plot the 3D sensor topography for the time window [0.035, 0.050] seconds, along with the brain and scalp:

load mesh_brain mesh_brain
load mesh_scalp mesh_scalp
load avg_squid avg_squid
load ctf275 ctf275

% do not select the reference sensors
index  = startsWith(ctf275.label, 'M');
pos275 = ctf275.chanpos(index, :);

% average the activity of interest in the time window [0.035 0.050] sec
I1 = nearest(avg_squid.time, 0.035);
I2 = nearest(avg_squid.time, 0.050);

selected_avg = mean(avg_squid.avg(:, I1:I2), 2);

% plot
figure
ft_plot_mesh(mesh_scalp, 'facealpha', 0.5, 'facecolor', 'skin', 'edgecolor', 'none')
hold on
ft_plot_mesh(mesh_brain, 'facecolor', 'brain', 'edgecolor', 'none')
hold on
ft_plot_topo3d(pos275, selected_avg, 'facealpha', 0.9)
camlight
view([90 0])

print -dpng analysis_topo3d_squid.png

OPM

We will coregister the OPMs with the MRI using an optical 3D scanner which captures the participantā€™s facial features along with the OPM helmet. First we read and plot the optical scan:

scan      = ft_read_headshape('sub-01/meg/opm/sub-01-ses-01_opticalscan.obj');
scan.unit = 'm'; % assign the correct unit

figure
hold on
ft_plot_headshape(scan);
ft_plot_axes(scan);
lighting gouraud
material dull
light

We assign a head-based coordinate system to the scan, using the fiducial method in which we click on the nasion, LPA and RPA in the displayed scan.

cfg          = [];
cfg.method   = 'fiducial';
cfg.coordsys = 'neuromag';
scan_aligned = ft_meshrealign(cfg, scan);

figure
hold on
ft_plot_headshape(scan_aligned);
ft_plot_axes(scan_aligned);
view([125 10]);
lighting gouraud
material dull
light

save scan_aligned scan_aligned

We remove irrelevant parts such as the body and cables. We use the ft_defacemesh function with a bounding box to keep only the desired head area.

cfg           = [];
cfg.method    = 'box';
cfg.selection = 'inside';
scan_head     = ft_defacemesh(cfg, scan_aligned);

figure
hold on
ft_plot_headshape(scan_head);
ft_plot_axes(scan_head);
view([125 10]);
lighting gouraud
material dull
light

print -dpng analysis_scan_head.png

We isolate the face region from the 3D scan using a bounding box. We will later align this face with the face extracted from the MRI.

cfg           = [];
cfg.method    = 'box';
cfg.selection = 'inside';
scan_face     = ft_defacemesh(cfg, scan_head);

figure
hold on
ft_plot_headshape(scan_face);
ft_plot_axes(scan_face);
view([125 10]);
lighting gouraud
material dull
light

print -dpng analysis_scan_face.png

Next, we isolate the helmet region from the 3D scan using a bounding box. We will later align this helmet with the reference helmet (i.e., the 3D model of the actual FieldLine helmet).

cfg           = [];
cfg.method    = 'box';
cfg.selection = 'outside';
scan_helmet   = ft_defacemesh(cfg, scan_head);

figure
hold on
ft_plot_headshape(scan_helmet);
ft_plot_axes(scan_helmet);
view([125 10]);
lighting gouraud
material dull
light

print -dpng analysis_scan_helmet.png

We load the MRI. As the same participant took part in both the SQUID and the OPM recordings, we can reuse the segmented MRI from the SQUID analysis to save time:

load mri_segmented mri_segmented % from the SQUID analysis

We align the face from the 3D scan with the face extracted from the MRI.

% create a mesh for the scalp
cfg             = [];
cfg.tissue      = 'scalp';
cfg.numvertices = 10000;
mri_face        = ft_prepare_mesh(cfg, mri_segmented);

% alignment
cfg               = [];
cfg.method        = 'interactive';
cfg.headshape     = mri_face;
cfg.meshstyle     = {'edgecolor', 'k', 'facecolor', 'skin'};
scan_face_aligned = ft_meshrealign(cfg, scan_face);

save scan_face_aligned scan_face_aligned

figure
hold on
ft_plot_headshape(mri_face, 'facealpha', 0.4);
ft_plot_mesh(scan_face_aligned, 'facecolor', 'skin');
view([125 10]);
lighting gouraud
material dull
light

print -dpng analysis_scan_face_aligned.png

We align the helmet from the 3D scan with the reference helmet. For this we use the 3D model of the actual FieldLine helmet. The reference helmet only contains the rim around the face.

helmet_rim = ft_read_headshape('sub-01/meg/opm/fieldlinebeta2_helmet_rim.mat');

cfg                   = [];
cfg.method            = 'interactive';
cfg.headshape         = helmet_rim;
cfg.meshstyle         = {'edgecolor', 'none', 'facecolor', [1 0.5 0.5]};
scan_helmet_aligned   = ft_meshrealign(cfg, scan_helmet);

save scan_helmet_aligned scan_helmet_aligned

figure
hold on
ft_plot_mesh(helmet_rim, 'edgecolor', 'none', 'facecolor', [0.5 0.5 1], 'facealpha', 0.4);
ft_plot_mesh(scan_helmet_aligned, 'edgecolor', 'none', 'facecolor', [1 0.5 0.5], 'facealpha', 0.4);
view([145 10]);
lighting gouraud
material dull
light

print -dpng analysis_scan_helmet_aligned.png

The three objects (the optical 3D scan, the face from the MRI and the template helmet) are initially all expressed in different coordinate systems. In the previous steps we have determined two pairwise transformations, which can be combined and used to align each of the objects to any other object.

Now we can use the transformation that aligns the face from the 3D scan with the face from the anatomical MRI and the transformation that aligns the helmet from the 3D scan with the reference helmet to calculate the transformation that aligns the reference helmet with the anatomical MRI.

transform_scan2helmet = scan_helmet_aligned.cfg.transform;
transform_scan2face   = scan_face_aligned.cfg.transform;
transform_helmet2face = transform_scan2face/transform_scan2helmet;

The transformation matrix transform_helmet2face can now be used to coregister the OPM sensors with the MRI, which aligns the OPM sensors with the head-based coordinate system. Before applying the transformation, we need to read the OPM sensors from the six fif files and combine them into a single sensor structure. In the FieldLine system the OPM sensors slide into the smart helmet; the fif file contains the actual position of the sensors relative to the helmet.

d = dir('sub-01/meg/opm/*.fif');

for i = 1:6
    sensfile = fullfile(d(i).folder, d(i).name);
    sens(i)  = ft_read_sens(sensfile);
end

save sens sens

cfg              = [];
sens_combined    = ft_appendsens(cfg, sens(1), sens(2), sens(3), sens(4), sens(5), sens(6)); % removes the duplicate channels

save sens_combined sens_combined

We apply the transformation to align the OPM sensors with the head-based coordinate system.

fieldlinebeta2_head = ft_transform_geometry(transform_helmet2face, sens_combined);

save fieldlinebeta2_head fieldlinebeta2_head

figure
hold on
ft_plot_sens(fieldlinebeta2_head, 'label', 'off');
ft_plot_headshape(mri_face, 'facecolor', [0.5 0.5 1], 'facealpha', 0.4, 'edgecolor', 'none');
view([125 10]);
lighting gouraud
material dull
light

print -dpng analysis_fieldlinebeta2_head.png

Lastly, we plot the 3D sensor topography for the time window [0.035, 0.050] seconds, along with the scalp:

load mesh_brain mesh_brain % from the SQUID analysis
load mesh_scalp mesh_scalp % from the SQUID analysis
load fieldlinebeta2_head fieldlinebeta2_head
load append_opm append_opm

[i_avg, i_sens] = match_str(append_opm.label, fieldlinebeta2_head.label);

% average the activity of interest in the time window [0.035 0.050] sec
I1 = nearest(append_opm.time, 0.035);
I2 = nearest(append_opm.time, 0.050);

selected_avg = mean(append_opm.avg(:, I1:I2), 2);

% plot
figure
ft_plot_mesh(mesh_scalp, 'facealpha', 0.5, 'facecolor', 'skin', 'edgecolor', 'none')
hold on
ft_plot_topo3d(fieldlinebeta2_head.chanpos(i_sens, :), selected_avg(i_avg), 'facealpha', 1)
camlight
view([90 0])

print -dpng analysis_topo3d_opm.png

Why do you think thereā€™s a gap on the right side of the topography? Hint: Interpolation.

How do the 3D sensor topographies of OPMs and SQUIDs differ, and what causes these differences?

Summary and suggested further reading

In this tutorial we analyzed OPM-MEG data from a left median nerve stimulation experiment, and we compared the preprocessing and coregistration steps with those for the same experiment recorded with a SQUID system. For the OPMs we removed environmental noise with HFC, and we combined the six sequential runs into a single virtual experiment with almost 144 sensors by renaming the duplicate channels, appending the data over the runs and adding NaNs for the missing sensors. We coregistered the OPMs with the anatomical MRI using an optical 3D scan of the participantā€™s face and helmet, combined the transformations to express the OPM sensors in the same coordinate system as the MRI, and plotted the 3D sensor topographies of the somatosensory evoked field for both the OPM and the SQUID system.

See also

For more information, see also

 

Tags: hangzhou2026