mne.preprocessing.find_bad_channels_maxwell¶

mne.preprocessing.find_bad_channels_maxwell(raw, limit=7.0, duration=5.0, min_count=5, return_scores=False, origin='auto', int_order=8, ext_order=3, calibration=None, cross_talk=None, coord_frame='head', regularize='in', ignore_ref=False, bad_condition='error', head_pos=None, mag_scale=100.0, skip_by_annotation=('edge', 'bad_acq_skip'), h_freq=40.0, extended_proj=(), verbose=None)[source]¶

Find bad channels using Maxwell filtering.

Parameters

rawinstance of Raw: Raw data to process.
limitfloat: Detection limit for noisy segments (default is 7.). Smaller values will find more bad channels at increased risk of including good ones. This value can be interpreted as the standard score of differences between the original and Maxwell-filtered data. See the Notes section for details.

Note

This setting only concerns noisy channel detection. The limit for flat channel detection currently cannot be controlled by the user. Flat channel detection is always run before noisy channel detection.
durationfloat: Duration of the segments into which to slice the data for processing, in seconds. Default is 5.
min_countint: Minimum number of times a channel must show up as bad in a chunk. Default is 5.
return_scoresbool: If True, return a dictionary with scoring information for each evaluated segment of the data. Default is False.

Warning

This feature is experimental and may change in a future version of MNE-Python without prior notice. Please report any problems and enhancement proposals to the developers.

New in version 0.21.
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.
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.
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.
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).
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.
h_freqfloat | None: The cutoff frequency (in Hz) of the low-pass filter that will be applied before processing the data. This defaults to 40., which should provide similar results to MaxFilter. If you do not wish to apply a filter, set this to None.
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, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

noisy_chslist

List of bad MEG channels that were automatically detected as being noisy among the good MEG channels.

flat_chslist

List of MEG channels that were detected as being flat in at least min_count segments.

scoresdict

A dictionary with information produced by the scoring algorithms. Only returned when return_scores is True. It contains the following keys:

ch_namesndarray, shape (n_meg,)
The names of the MEG channels. Their order corresponds to the order of rows in the scores and limits arrays.
ch_typesndarray, shape (n_meg,)
The types of the MEG channels in ch_names ('mag', 'grad').
binsndarray, shape (n_windows, 2)
The inclusive window boundaries (start and stop; in seconds) used to calculate the scores.
scores_flatndarray, shape (n_meg, n_windows)
The scores for testing whether MEG channels are flat. These values correspond to the standard deviation of a segment. See the Notes section for details.
limits_flatndarray, shape (n_meg, 1)
The score thresholds (in standard deviation) above which a segment was classified as “flat”.
scores_noisyndarray, shape (n_meg, n_windows)
The scores for testing whether MEG channels are noisy. These values correspond to the standard score of a segment. See the Notes section for details.
limits_noisyndarray, shape (n_meg, 1)
The score thresholds (in standard scores) above which a segment was classified as “noisy”.

Note

The scores and limits for channels marked as bad in the input data will be set to np.nan.

Examples using `mne.preprocessing.find_bad_channels_maxwell`¶

mne.preprocessing.find_bad_channels_maxwell¶

Examples using mne.preprocessing.find_bad_channels_maxwell¶

Examples using `mne.preprocessing.find_bad_channels_maxwell`¶