meeg_utils.steps.Baseline#

class meeg_utils.steps.Baseline(baseline=(None, 0.0), mode='mean', *, scale='epoch', max_std_error=0.1)[source]#

Baseline correction of epochs or evoked data.

Parameters:

  • baseline (tuple) – Baseline interval (start, end) in seconds; None means the start or end of the epoch.

  • mode ({"mean", "ratio", "logratio", "percent", "zscore", "zlogratio"}) – "mean" subtracts the baseline mean (mne.Epochs.apply_baseline()); the others are mne.baseline.rescale().

  • scale ({"epoch", "pooled"}) – For "zscore": divide by each epoch’s own baseline standard deviation (MNE), or by the standard deviation of the baseline pooled over all epochs (per channel), which is far more precise for short baselines.

  • max_std_error (float) – Per-epoch z-scoring is refused when the baseline is too short to estimate a standard deviation: with n samples its relative standard error is about 1 / sqrt(2 (n - 1)) (for Gaussian noise). The default, 10 %, requires at least 51 samples.

qc_#

mode, window (s) and n_samples in the baseline.

Type:

dict

__init__(baseline=(None, 0.0), mode='mean', *, scale='epoch', max_std_error=0.1)[source]#

Methods

__init__([baseline, mode, scale, max_std_error])

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.epochs.BaseEpochs'>, <class 'mne.evoked.Evoked'>)#

Data types the step accepts.