meeg_utils.steps.BridgedElectrodes

class meeg_utils.steps.BridgedElectrodes(*, lm_cutoff=16.0, epoch_threshold=0.5, l_freq=0.5, h_freq=30.0, epoch_duration=2.0, min_correlation=0.98, confirm_band=(15.0, 30.0), bad_limit=4, large_groups='raise')

Detect bridged EEG electrodes and interpolate them.

Detection is meeg_utils.qc.Bridging (MNE’s compute_bridged_electrodes()); repair is mne.preprocessing.interpolate_bridged_electrodes(), which places a virtual electrode at the centre of each bridged group, carrying the group’s average signal, and interpolates the bridged electrodes from it and their neighbours. Channel names and order are unchanged.

Parameters:

• lm_cutoff (float) – Detection parameters (see meeg_utils.qc.Bridging).

• epoch_threshold (float) – Detection parameters (see meeg_utils.qc.Bridging).

• l_freq (float) – Detection parameters (see meeg_utils.qc.Bridging).

• h_freq (float) – Detection parameters (see meeg_utils.qc.Bridging).

• epoch_duration (float) – Detection parameters (see meeg_utils.qc.Bridging).

• min_correlation (float | None) – Detection parameters (see meeg_utils.qc.Bridging).

• confirm_band (tuple[float, float]) – Detection parameters (see meeg_utils.qc.Bridging).

• bad_limit (int) – Groups of more bridged electrodes than this cannot be interpolated reliably (MNE default 4).

• large_groups ({"raise", "bad"}) – What to do with such groups: raise (default), or mark their electrodes bad (info["bads"]) and interpolate the other groups.

check_

The detection, with its metrics and figures.

Type:

meeg_utils.qc.Bridging

pairs_

Bridged channel pairs (names).

Type:

list of tuple

qc_

pairs, groups, interpolated and marked_bad channels.

Type:

dict

Notes

Needs electrode positions. Figures (plot()): those of the detection, "topomap" and "distances".

__init__(*, lm_cutoff=16.0, epoch_threshold=0.5, l_freq=0.5, h_freq=30.0, epoch_duration=2.0, min_correlation=0.98, confirm_band=(15.0, 30.0), bad_limit=4, large_groups='raise')

Methods

__init__(*[, lm_cutoff, epoch_threshold, ...])
fit(inst[, y, system])	Learn the step's state from the data.
fit_transform(inst[, y, system, copy])	Fit the step on the data and transform the same data.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
plot([kind, inst])	Draw quality-control figures.
set_fit_request(*[, inst, system])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, copy, inst])	Configure whether metadata should be requested to be passed to the transform method.
transform(inst, *[, copy])	Apply the fitted step to the data.

Attributes

accepts	Data types the step accepts.
adds_annotations	Whether the step may add BAD_ annotations (existing ones are kept).
changes_channels	Whether the step may add, drop or reorder channels.
changes_times	Whether the step may change the time axis (e.g. resampling, cropping).
drops_epochs	Whether the step may drop epochs (e.g. artifact rejection).
plot_kinds	kind -> whether it needs data (inst).
returns	Data type the step returns; None means the same type as its input.
systems	Acquisition systems the step applies to; None means all systems.

accepts: ClassVar[tuple[type, ...]] = (<class 'mne.io.base.BaseRaw'>, <class 'mne.epochs.BaseEpochs'>, <class 'mne.evoked.Evoked'>)

Data types the step accepts.

systems: ClassVar[frozenset[str] | None] = None

Acquisition systems the step applies to; None means all systems.

plot_kinds: ClassVar[dict[str, bool]] = {'distances': False, 'topomap': False}

kind -> whether it needs data (inst). Each kind is drawn by a _plot_<kind>(inst, **kwargs) method.

Type:

Figures the object can draw once fitted