mne.preprocessing.compute_proj_hfc#
- mne.preprocessing.compute_proj_hfc(info, order=1, picks='meg', exclude='bads', *, accuracy='accurate', verbose=None)[source]#
Generate projectors to perform homogeneous/harmonic correction to data.
Remove evironmental fields from magentometer data by assuming it is explained as a homogeneous 1 or harmonic field 2. Useful for arrays of OPMs.
- Parameters
- info
mne.Info|None The
mne.Infoobject with information about the sensors and methods of measurement.- order
int The order of the spherical harmonic basis set to use. Set to 1 to use only the homogeneous field component (default), 2 to add gradients, 3 to add quadrature terms etc.
- picks
str| array_like |slice|None Channels to include. Default of
'meg'(same as None) will select all non-reference MEG channels. Use('meg', 'ref_meg')to include reference sensors as well.- exclude
list| ‘bads’ List of channels to exclude from HFC, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”). Specify
'bads'(the default) to exclude all channels marked as bad.- accuracy
str Can be
"point","normal"or"accurate"(default), defines which level of coil definition accuracy is used to generate model.- verbose
bool|str|int|None Control verbosity of the logging output. If
None, use the default verbosity level. See the logging documentation andmne.verbose()for details. Should only be passed as a keyword argument.
- info
- Returns
- projs
listofProjection List of computed projection vectors.
- projs
See also
Notes
To apply the projectors to a dataset, use
inst.add_proj(projs).apply_proj().New in v1.4.
References
- 1
Tim M. Tierney, Nicholas Alexander, Stephanie Mellor, Niall Holmes, Robert Seymour, George C. O’Neill, Eleanor A. Maguire, and Gareth R. Barnes. Modelling optically pumped magnetometer interference in meg as a spatially homogeneous magnetic field. NeuroImage, 2021. doi:10.1016/j.neuroimage.2021.118484.
- 2
Tim M. Tierney, George C. Mellor, Stephanie nd O’Neill, Ryan C. Timms, and Gareth R. Barnes. Spherical harmonic based noise rejection and neuronal sampling with multi-axis opms. NeuroImage, 2022. doi:10.1016/j.neuroimage.2022.119338.
Examples using mne.preprocessing.compute_proj_hfc#
Preprocessing optically pumped magnetometer (OPM) MEG data