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='progressbar')[source]¶

RANSAC algorithm to find bad sensors and repair them.

Methods

fit
fit_transform
transform

__init__(self, n_resample=50, min_channels=0.25, min_corr=0.75, unbroken_time=0.4, n_jobs=1, random_state=435656, picks=None, verbose='progressbar')[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

To seed or not the random number generator.

picksndarray, shape(n_channels) | None

The channels to be considered for autoreject. If None, defaults to data channels {‘meg’, ‘eeg’}.

verbose‘tqdm’, ‘tqdm_notebook’, ‘progressbar’ or False

The verbosity of progress messages. If ‘progressbar’, use mne.utils.ProgressBar. If ‘tqdm’, use tqdm.tqdm. If ‘tqdm_notebook’, use tqdm.tqdm_notebook. 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__(self[, n_resample, min_channels, …])	Implements RAndom SAmple Consensus (RANSAC) method to detect bad sensors.
fit(self, epochs)
fit_transform(self, epochs)
transform(self, epochs)