## Should I apply ICA first or autoreject first?¶

ICA solutions can be affected by high amplitude artifacts, therefore we recommend first using autoreject to detect the bad segments, then applying ICA, and finally interpolating the bad data.

To ignore bad segments using autoreject (local), we could do:

>>> ar = AutoReject()
>>> _, reject_log = ar.fit(epochs).transform(epochs, return_log=True)


or use autoreject (global):

>>> reject = get_rejection_threshold(epochs)
>>> ica.fit(epochs, reject=reject)


This option can be more preferred if we would like to fit ICA on the raw data, and not on the epochs. After this, we can apply ICA:

>>> ica.exclude = [5, 7]  # exclude EOG components
>>> ica.transform(epochs)


Finally, autoreject could be applied to clean the data:

>>> ar = AutoReject()
>>> epochs_clean = ar.fit_transform(epochs)


Autoreject is not meant for eyeblink artifacts since it affects neighboring sensors. Indeed, a spatial filtering method like ICA is better suited for this.

## How do I manually set the n_interpolate and consensus parameter?¶

If you do not want autoreject to select a parameter for you, simply pass it as a list of a single element:

>>> ar = AutoReject(n_interpolate=[1], consensus_percs=[0.6])


Note this will still run a cross-validation loop to generate the validation score.

## Is it possible to get only bad sensor annotations and not interpolate?¶

Yes! Simply do:

>>> ar.fit(epochs)
>>> reject_log = ar.get_reject_log(epochs)


No need to run ar.transform(epochs) in this case.