Getting started with FieldLine OPM data

FieldLine is a company located in Boulder, Colorado that develops OPM sensors and complete OPM-based MEG systems. The data from their current systems is stored in the *.fif format, which is developed and used by Neuromag/Elekta/Megin. The *.fif file format is already supported by FieldTrip, which means that no special functions are needed for reading the data.

cfg = [];
cfg.dataset = 'MedianNerve_RestingStateEyesClosed.fif';
data = ft_preprocessing(cfg);

cfg = [];
cfg.preproc.demean = 'yes';
cfg.ylim = [-1 1] * 1e-11;
cfg.blocksize = 30; % seconds
ft_databrowser(cfg, data);

There are a number of differences though, which are relevant for processing FieldLine data in more detail. One difference is how events or triggers are represented and detected. Another difference is in the procedure used to record the position of the OPM sensors relative to the head, which is relevant for topographic plotting and for the coregistration between MEG and MRI. Furthermore, it can be the case that you have multiple recordings with a small number of OPM sensors on different locations, and that you want to merge those recordings.

Reading and processing triggers

Depending on the generation of the FieldLine OPM system that you are working with, the triggers may be represented as an analog channel (with voltages) or as a digital channel.

In this case the recording is from a FieldLine v2 system and the experiment consisted of a median nerve stimulation on the right wrist. Each stimulus is coded with an analog trigger represented in the Input-1 channel.

filename = 'MedianNerve_StimBreakStim2min_Pos1.fif';
hdr = ft_read_header(filename);

disp(hdr.label)
    {'Input-1'    }
    {'00:01-BZ_OL'}
    {'00:02-BZ_OL'}
    {'00:03-BZ_OL'}
    {'00:04-BZ_OL'}
    {'00:05-BZ_OL'}
    {'00:06-BZ_OL'}
    {'00:07-BZ_OL'}
    {'00:08-BZ_OL'}

% determine the segments of interest around the triggers (aka the trials)
cfg = [];
cfg.dataset = filename;
cfg.trialdef.eventtype = 'Input-1';
cfg.trialdef.prestim = 0.1;
cfg.trialdef.poststim = 0.3;
cfg = ft_definetrial(cfg);

% read the segments of interest (aka the trials)
cfg.channel = '00*';
cfg.demean = 'yes';
cfg.baselinewindow = [-inf 0];
data = ft_preprocessing(cfg);

% remove large variance trials
cfg = [];
cfg.method = 'summary';
data_clean = ft_rejectvisual(cfg, data);

% average over trials and plot the ERF
cfg = [];
timelock = ft_timelockanalysis(cfg, data_clean);

plot(timelock.time, timelock.avg)

As there is quite some drift in the signal, it is better to apply a filter to the continuous data prior to segmenting. That results in the following alternate preprocessing script, similar to the tutorial on processing continuous EEG or MEG data.

% read the continuous data
cfg = [];
cfg.dataset = filename;
cfg.channel = '00*';
cfg.demean = 'yes';
cfg.baselinewindow = [-inf inf];
cfg.hpfilter = 'yes';
cfg.hpfreq = 1;
data_continuous = ft_preprocessing(cfg);

% determine the segments of interest around the triggers (aka the trials)
cfg = [];
cfg.dataset = filename;
cfg.trialdef.eventtype = 'Input-1';
cfg.trialdef.prestim = 0.1;
cfg.trialdef.poststim = 0.3;
cfg = ft_definetrial(cfg);

% do the actual segmentation of the data
data_segmented = ft_redefinetrial(cfg, data_continuous);

% remove large variance trials
cfg = [];
cfg.method = 'summary';
data_clean = ft_rejectvisual(cfg, data_segmented);

% average over trials and plot the ERF
cfg = [];
timelock = ft_timelockanalysis(cfg, data_clean);

plot(timelock.time, timelock.avg)

Topographic plotting

When we look at this FieldLine v2 recording, we see channel names like 00:01-BZ_OL and 00:02-BZ_OL. These channels correspond to the hardware sensors, i.e. the OPMs, and in this case each OPM has only one channel in the axial (or normal) direction, indicated by “BZ”.

The data structure returned by ft_preprocessing or the header from ft_read_header include the grad field, which describes the sensor positions (see also this FAQ). However, in this case the recording software did not know in which slots of the 3D-printed helmet the OPMs were placed. The data.grad.coilpos and data.grad.coilori fields that are obtained from the fif file also don’t make sense.

In this specific recording the OPM sensors were placed in an early version of the FieldLine smart helmet that the company refers to as the “Alpha1” version. The specific helmet has 107 slots for OPM sensors, but of course not all slots need to be filled. The FieldTrip templates includes a detailed description of this helmet, and there is a layout for 2D topographic plotting.

During acquisition of the data, an Excel table was maintained to map the channel numbers in the electronics chassis, to the OPM sensors, and to the corresponding slots in the 3D-printed helmet.

For topographic plotting the channel names (in data.label) should match the locations on the head. To keep the script clean and reproducible, we will not go in the data structure and manually rename the channels to match, but we will use a montage, similar to how EEG and iEEG channels can be renamed, combined, added, and subtracted.

montage = [];
montage.labelold = {
  '00:01-BZ_OL'
  '00:02-BZ_OL'
  '00:03-BZ_OL'
  '00:04-BZ_OL'
  '00:05-BZ_OL'
  '00:06-BZ_OL'
  '00:07-BZ_OL'
  '00:08-BZ_OL'
  }';
montage.labelnew = {
  'FL30'
  'FL21'
  'FL20'
  'FL23'
  'FL36'
  'FL35'
  'FL34'
  'FL84'
};
montage.tra = eye(8); % the data is simply copied one-to-one

% rename the channels in the raw data
cfg = [];
cfg.montage = montage;
data_helmetpos = ft_preprocessing(cfg, data);

% or rename the channels in the timelocked ERF data
timelock_helmetpos = ft_preprocessing(cfg, timelock)

% plot the ERFs on the corresponding position in the helmet
cfg = [];
cfg.layout = 'fieldlinealpha1_helmet.mat';
cfg.showoutline = 'yes';
ft_multiplotER(cfg, timelock_helmetpos)

Merging multiple recordings

In this experiment three subsequent recordings were performed on the same participant in which the 8 OPM sensors were moved to different positions in the Alpha1 helmet. Each recording is stored in a different fif file. Using the example above, we can construct three raw data structures with the trials. After removing the noisy trials in each of the three recordings, we can average each of the recordings.

Since channels in a FieldTrip data structure are required have a unique channel name, we cannot immediately use ft_appenddata or ft_appendtimelock to concatenate the data along the channel direction; we first have to rename the channels to make them unique. Using three separate montages to map (rename) the channels in each recording onto the corresponding names of the slots in the 3D printed helmet, we get raw data (or timelock) structures with unique channel names that can be appended.

Coregistration

There is a dedicated tutorial that deals with various ways to coregister the OPM sensors with the head and the anatomical MRI.