/
_regress.py
87 lines (78 loc) · 3.34 KB
/
_regress.py
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# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from ..epochs import BaseEpochs
from ..io.pick import _picks_to_idx
from ..io.base import BaseRaw
from ..utils import _check_preload, _validate_type, _check_option, verbose
@verbose
def regress_artifact(inst, picks=None, picks_artifact='eog', betas=None,
copy=True, verbose=None):
"""Remove artifacts using regression based on reference channels.
Parameters
----------
inst : instance of Epochs | Raw
The instance to process.
%(picks_good_data)s
picks_artifact : array-like | str
Channel picks to use as predictor/explanatory variables capturing
the artifact of interest (default is "eog").
betas : ndarray, shape (n_picks, n_picks_ref) | None
The regression coefficients to use. If None (default), they will be
estimated from the data.
copy : bool
If True (default), copy the instance before modifying it.
%(verbose)s
Returns
-------
inst : instance of Epochs | Raw
The processed data.
betas : ndarray, shape (n_picks, n_picks_ref)
The betas used during regression.
Notes
-----
To implement the method outlined in :footcite:`GrattonEtAl1983`,
remove the evoked response from epochs before estimating the
regression coefficients, then apply those regression coefficients to the
original data in two calls like (here for a single-condition ``epochs``
only):
>>> epochs_no_ave = epochs.copy().subtract_evoked() # doctest:+SKIP
>>> _, betas = mne.preprocessing.regress(epochs_no_ave) # doctest:+SKIP
>>> epochs_clean, _ = mne.preprocessing.regress(epochs, betas=betas) # doctest:+SKIP
References
----------
.. footbibliography::
""" # noqa: E501
_check_preload(inst, 'regress')
_validate_type(inst, (BaseEpochs, BaseRaw), 'inst', 'Epochs or Raw')
picks = _picks_to_idx(inst.info, picks, none='data')
picks_artifact = _picks_to_idx(inst.info, picks_artifact)
if np.in1d(picks_artifact, picks).any():
raise ValueError('picks_artifact cannot be contained in picks')
inst = inst.copy() if copy else inst
artifact_data = inst._data[..., picks_artifact, :]
ref_data = artifact_data - np.mean(artifact_data, -1, keepdims=True)
if ref_data.ndim == 3:
ref_data = ref_data.transpose(1, 0, 2).reshape(len(picks_artifact), -1)
cov = np.dot(ref_data, ref_data.T)
# process each one separately to reduce memory load
betas_shape = (len(picks), len(picks_artifact))
if betas is None:
betas = np.empty(betas_shape)
estimate = True
else:
estimate = False
betas = np.asarray(betas, dtype=float)
_check_option('betas.shape', betas.shape, (betas_shape,))
for pi, pick in enumerate(picks):
this_data = inst._data[..., pick, :] # view
orig_shape = this_data.shape
if estimate:
# subtract mean over time from every trial/channel
cov_data = this_data - np.mean(this_data, -1, keepdims=True)
cov_data = cov_data.reshape(1, -1)
betas[pi] = np.linalg.solve(cov, np.dot(ref_data, cov_data.T)).T[0]
# subtract weighted (demeaned) eye channels from channel
this_data -= (betas[pi] @ ref_data).reshape(orig_shape)
return inst, betas