class_docstr: Remove EOG artifact signals from other channels by regression.
  
  Employs linear regression to remove signals captured by some channels,
  typically EOG, as described in :footcite:`GrattonEtAl1983`. You can also
  choose to fit the regression coefficients on evoked blink/saccade data and
  then apply them to continuous data, as described in
  :footcite:`CroftBarry2000`.
  
  Parameters
  ----------
  %(picks_good_data)s
  exclude : list | 'bads'
      List of channels to exclude from the regression, only used when picking
      based on types (e.g., exclude="bads" when picks="meg").
      Specify ``'bads'`` (the default) to exclude all channels marked as bad.
  picks_artifact : array-like | str
      Channel picks to use as predictor/explanatory variables capturing
      the artifact of interest (default is "eog").
  proj : bool
      Whether to automatically apply SSP projection vectors before fitting
      and applying the regression. Default is ``True``.
  
  Attributes
  ----------
  coef_ : ndarray, shape (n, n)
      The regression coefficients. Only available after fitting.
  info_ : Info
      Channel information corresponding to the regression weights.
      Only available after fitting.
  picks : array-like | str
      Channels to perform the regression on.
  exclude : list | 'bads'
      Channels to exclude from the regression.
  picks_artifact : array-like | str
      The channels designated as containing the artifacts of interest.
  proj : bool
      Whether projections will be applied before performing the regression.
  
  Notes
  -----
  .. versionadded:: 1.2
  
  References
  ----------
  .. footbibliography::
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docstr: Save the regression model to an HDF5 file.
  
  Parameters
  ----------
  fname : path-like
      The file to write the regression weights to. Should end in ``.h5``.
  %(overwrite)s
docstr_tok: ["Save","the","regression","model","to","an","HDF5","file",".","Parameters","--","--","--","--","--","fname",":","path-like","The","file","to","write","the","regression","weights","to",".","Should","end","in","``",".h5","``",".","%","(","overwrite",")","s"]
code: def save(self, fname, overwrite=False):
      """Save the regression model to an HDF5 file.
  
      Parameters
      ----------
      fname : path-like
          The file to write the regression weights to. Should end in ``.h5``.
      %(overwrite)s
      """
      _, write_hdf5 = _import_h5io_funcs()
      _validate_type(fname, "path-like", "fname")
      fname = _check_fname(fname, overwrite=overwrite, name="fname")
      write_hdf5(fname, self.__dict__, overwrite=overwrite)
  
file_path: mne-python/mne/preprocessing/_regress.py
imports: import numpy
  from .._fiff.pick import _picks_to_idx
  from ..defaults import _BORDER_DEFAULT, _EXTRAPOLATE_DEFAULT, _INTERPOLATION_DEFAULT
  from ..epochs import BaseEpochs
  from ..evoked import Evoked
  from ..io import BaseRaw
  from ..minimum_norm.inverse import _needs_eeg_average_ref_proj
  from ..utils import _check_fname, _check_option, _check_preload, _import_h5io_funcs, _validate_type, copy_function_doc_to_method_doc, fill_doc, verbose
  from ..viz import plot_regression_weights
label_desc: Use image node_id 7 for calling the EOGRegression obj's underlying member method code with example usage: obj.save(fname, overwrite) without return types