mne.preprocessing.maxwell_filter#

mne.preprocessing.maxwell_filter(raw, origin='auto', int_order=8, ext_order=3, calibration=None, cross_talk=None, st_duration=None, st_correlation=0.98, coord_frame='head', destination=None, regularize='in', ignore_ref=False, bad_condition='error', head_pos=None, st_fixed=True, st_only=False, mag_scale=100.0, skip_by_annotation=('edge', 'bad_acq_skip'), extended_proj=(), verbose=None)[source]#

Maxwell filter data using multipole moments.

Parameters

rawinstance of mne.io.Raw: Data to be filtered.

Warning

It is critical to mark bad channels in raw.info['bads'] prior to processing in order to prevent artifact spreading. Manual inspection and use of find_bad_channels_maxwell() is recommended.
originarray-like, shape (3,) | str: Origin of internal and external multipolar moment space in meters. The default is 'auto', which means (0., 0., 0.) when coord_frame='meg', and a head-digitization-based origin fit using fit_sphere_to_headshape() when coord_frame='head'. If automatic fitting fails (e.g., due to having too few digitization points), consider separately calling the fitting function with different options or specifying the origin manually.
int_orderint: Order of internal component of spherical expansion.
ext_orderint: Order of external component of spherical expansion.
calibrationstr | None: Path to the '.dat' file with fine calibration coefficients. File can have 1D or 3D gradiometer imbalance correction. This file is machine/site-specific.
cross_talkstr | None: Path to the FIF file with cross-talk correction information.
st_durationfloat | None: If not None, apply spatiotemporal SSS with specified buffer duration (in seconds). MaxFilter™’s default is 10.0 seconds in v2.2. Spatiotemporal SSS acts as implicitly as a high-pass filter where the cut-off frequency is 1/st_duration Hz. For this (and other) reasons, longer buffers are generally better as long as your system can handle the higher memory usage. To ensure that each window is processed identically, choose a buffer length that divides evenly into your data. Any data at the trailing edge that doesn’t fit evenly into a whole buffer window will be lumped into the previous buffer.
st_correlationfloat: Correlation limit between inner and outer subspaces used to reject ovwrlapping intersecting inner/outer signals during spatiotemporal SSS.
coord_framestr: The coordinate frame that the origin is specified in, either 'meg' or 'head'. For empty-room recordings that do not have a head<->meg transform info['dev_head_t'], the MEG coordinate frame should be used.
destinationstr | array-like, shape (3,) | None: The destination location for the head. Can be None, which will not change the head position, or a string path to a FIF file containing a MEG device<->head transformation, or a 3-element array giving the coordinates to translate to (with no rotations). For example, destination=(0, 0, 0.04) would translate the bases as --trans default would in MaxFilter™ (i.e., to the default head location).
regularizestr | None: Basis regularization type, must be “in” or None. “in” is the same algorithm as the “-regularize in” option in MaxFilter™.
ignore_refbool: If True, do not include reference channels in compensation. This option should be True for KIT files, since Maxwell filtering with reference channels is not currently supported.
bad_conditionstr: How to deal with ill-conditioned SSS matrices. Can be “error” (default), “warning”, “info”, or “ignore”.
head_posarray | None: If array, movement compensation will be performed. The array should be of shape (N, 10), holding the position parameters as returned by e.g. read_head_pos.

New in version 0.12.
st_fixedbool: If True (default), do tSSS using the median head position during the st_duration window. This is the default behavior of MaxFilter and has been most extensively tested.

New in version 0.12.
st_onlybool: If True, only tSSS (temporal) projection of MEG data will be performed on the output data. The non-tSSS parameters (e.g., int_order, calibration, head_pos, etc.) will still be used to form the SSS bases used to calculate temporal projectors, but the output MEG data will only have temporal projections performed. Noise reduction from SSS basis multiplication, cross-talk cancellation, movement compensation, and so forth will not be applied to the data. This is useful, for example, when evoked movement compensation will be performed with average_movements().

New in version 0.12.
mag_scalefloat | str: The magenetometer scale-factor used to bring the magnetometers to approximately the same order of magnitude as the gradiometers (default 100.), as they have different units (T vs T/m). Can be 'auto' to use the reciprocal of the physical distance between the gradiometer pickup loops (e.g., 0.0168 m yields 59.5 for VectorView).

New in version 0.13.
skip_by_annotationstr | list of str: If a string (or list of str), any annotation segment that begins with the given string will not be included in filtering, and segments on either side of the given excluded annotated segment will be filtered separately (i.e., as independent signals). The default ('edge', 'bad_acq_skip') will separately filter any segments that were concatenated by mne.concatenate_raws() or mne.io.Raw.append(), or separated during acquisition. To disable, provide an empty list.

New in version 0.17.
extended_projlist: The empty-room projection vectors used to extend the external SSS basis (i.e., use eSSS).

New in version 0.21.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns

raw_sssinstance of mne.io.Raw: The raw data with Maxwell filtering applied.

See also

mne.preprocessing.annotate_amplitude
mne.preprocessing.find_bad_channels_maxwell
mne.chpi.filter_chpi
mne.chpi.read_head_pos
mne.epochs.average_movements

Notes

New in version 0.11.

Some of this code was adapted and relicensed (with BSD form) with permission from Jussi Nurminen. These algorithms are based on work from 1 and 2. It will likely use multiple CPU cores, see the FAQ for more information.

Warning

Maxwell filtering in MNE is not designed or certified for clinical use.

Compared to the MEGIN MaxFilter™ software, the MNE Maxwell filtering routines currently provide the following features:

Feature	MNE	MaxFilter
Maxwell filtering software shielding	✓	✓
Bad channel reconstruction	✓	✓
Cross-talk cancellation	✓	✓
Fine calibration correction (1D)	✓	✓
Fine calibration correction (3D)	✓
Spatio-temporal SSS (tSSS)	✓	✓
Coordinate frame translation	✓	✓
Regularization using information theory	✓	✓
Movement compensation (raw)	✓	✓
Movement compensation (`epochs`)	✓
`cHPI subtraction`	✓	✓
Double floating point precision	✓
Seamless processing of split (`-1.fif`) and concatenated files	✓
Automatic bad channel detection (`find_bad_channels_maxwell()`)	✓	✓
Head position estimation (`compute_head_pos()`)	✓	✓
Certified for clinical use		✓
Extended external basis (eSSS)	✓

Epoch-based movement compensation is described in 1.

Use of Maxwell filtering routines with non-Neuromag systems is currently experimental. Worse results for non-Neuromag systems are expected due to (at least):

Missing fine-calibration and cross-talk cancellation data for other systems.
Processing with reference sensors has not been vetted.
Regularization of components may not work well for all systems.
Coil integration has not been optimized using Abramowitz/Stegun definitions.

Note

Various Maxwell filtering algorithm components are covered by patents owned by MEGIN. These patents include, but may not be limited to:

US2006031038 (Signal Space Separation)
US6876196 (Head position determination)
WO2005067789 (DC fields)
WO2005078467 (MaxShield)
WO2006114473 (Temporal Signal Space Separation)

These patents likely preclude the use of Maxwell filtering code in commercial applications. Consult a lawyer if necessary.

Currently, in order to perform Maxwell filtering, the raw data must not have any projectors applied. During Maxwell filtering, the spatial structure of the data is modified, so projectors are discarded (unless in st_only=True mode).

References

1(1,2): Samu Taulu and Matti Kajola. Presentation of electromagnetic multichannel data: the signal space separation method. Journal of Applied Physics, 97(12):124905, 2005. doi:10.1063/1.1935742.
2: Samu Taulu and Juha Simola. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology, 51(7):1759–1768, 2006. doi:10.1088/0031-9155/51/7/008.

Examples using `mne.preprocessing.maxwell_filter`#

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

Brainstorm CTF phantom dataset tutorial

Maxwell filter data with movement compensation

mne.preprocessing.equalize_bads

mne.preprocessing.oversampled_temporal_projection

mne.preprocessing.maxwell_filter#

Examples using mne.preprocessing.maxwell_filter#

Examples using `mne.preprocessing.maxwell_filter`#