autoreject.Ransac¶

class autoreject.Ransac(n_resample=50, min_channels=0.25, min_corr=0.75, unbroken_time=0.4, n_jobs=1, random_state=435656, picks=None, verbose=True)[source]¶

RANSAC algorithm to find bad sensors and repair them.

__init__(n_resample=50, min_channels=0.25, min_corr=0.75, unbroken_time=0.4, n_jobs=1, random_state=435656, picks=None, verbose=True)[source]¶

Implements RAndom SAmple Consensus (RANSAC) method to detect bad sensors.

Parameters

n_resampleint

Number of times the sensors are resampled.

min_channelsfloat

Fraction of sensors for robust reconstruction.

min_corrfloat

Cut-off correlation for abnormal wrt neighbours.

unbroken_timefloat

Cut-off fraction of time sensor can have poor RANSAC predictability.

n_jobsint

Number of parallel jobs.

random_stateNone | int | np.random.RandomState

The seed of the pseudo random number generator to use.

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel name strings (e.g., ['MEG0111', 'MEG2623']) will pick the given channels. None (default) will pick data channels {‘meg’, ‘eeg’}. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

verbosebool

The verbosity of progress messages. If False, suppress all output messages.

Notes

The window_size is automatically set to the epoch length.

References

[1] Bigdely-Shamlo, Nima, et al.

“The PREP pipeline: standardized preprocessing for large-scale EEG analysis.” Frontiers in neuroinformatics 9 (2015).

[2] Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and

Alexandre Gramfort, “Autoreject: Automated artifact rejection for MEG and EEG.” arXiv preprint arXiv:1612.08194, 2016.

Methods

__init__([n_resample, min_channels, ...])	Implements RAndom SAmple Consensus (RANSAC) method to detect bad sensors.
fit(epochs)
fit_transform(epochs)
transform(epochs)