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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import collections.abc as abc
from functools import partial
import numpy as np
from .._fiff.meas_info import Info
from ..cov import Covariance
from ..decoding._covs_ged import _xdawn_estimate
from ..decoding._mod_ged import _xdawn_mod
from ..decoding.base import _GEDTransformer
from ..utils import _validate_type, fill_doc
@fill_doc
class XdawnTransformer(_GEDTransformer):
"""Implementation of the Xdawn Algorithm compatible with scikit-learn.
Xdawn is a spatial filtering method designed to improve the signal
to signal + noise ratio (SSNR) of the event related responses. Xdawn was
originally designed for P300 evoked potential by enhancing the target
response with respect to the non-target response. This implementation is a
generalization to any type of event related response.
.. note:: XdawnTransformer does not correct for epochs overlap. To correct
overlaps see `mne.preprocessing.Xdawn`.
Parameters
----------
n_components : int (default 2)
The number of components to decompose the signals.
reg : float | str | None (default None)
If not None (same as ``'empirical'``, default), allow
regularization for covariance estimation.
If float, shrinkage is used (0 <= shrinkage <= 1).
For str options, ``reg`` will be passed to ``method`` to
:func:`mne.compute_covariance`.
signal_cov : None | Covariance | array, shape (n_channels, n_channels)
The signal covariance used for whitening of the data.
if None, the covariance is estimated from the epochs signal.
cov_method_params : dict | None
Parameters to pass to :func:`mne.compute_covariance`.
.. versionadded:: 0.16
restr_type : "restricting" | "whitening" | None
Restricting transformation for covariance matrices before performing
generalized eigendecomposition.
If "restricting" only restriction to the principal subspace of signal_cov
will be performed.
If "whitening", covariance matrices will be additionally rescaled according
to the whitening for the signal_cov.
If None, no restriction will be applied. Defaults to None.
.. versionadded:: 1.11
info : mne.Info | None
The mne.Info object with information about the sensors and methods of
measurement used for covariance estimation and generalized
eigendecomposition.
If None, one channel type and no projections will be assumed and if
rank is dict, it will be sum of ranks per channel type.
Defaults to None.
.. versionadded:: 1.11
%(rank_full)s
.. versionadded:: 1.11
Attributes
----------
classes_ : array, shape (n_classes)
The event indices of the classes.
filters_ : array, shape (n_channels, n_channels)
The Xdawn components used to decompose the data for each event type.
patterns_ : array, shape (n_channels, n_channels)
The Xdawn patterns used to restore the signals for each event type.
See Also
--------
CSP, SPoC, SSD
"""
def __init__(
self,
n_components=2,
reg=None,
signal_cov=None,
cov_method_params=None,
*,
restr_type=None,
info=None,
rank="full",
):
self.n_components = n_components
self.signal_cov = signal_cov
self.reg = reg
self.cov_method_params = cov_method_params
self.restr_type = restr_type
self.info = info
self.rank = rank
cov_callable = partial(
_xdawn_estimate,
reg=reg,
cov_method_params=cov_method_params,
R=signal_cov,
info=info,
rank=rank,
)
super().__init__(
n_components=n_components,
cov_callable=cov_callable,
mod_ged_callable=_xdawn_mod,
dec_type="multi",
restr_type=restr_type,
)
def __sklearn_tags__(self):
"""Tag the transformer."""
tags = super().__sklearn_tags__()
tags.target_tags.required = True
return tags
def _validate_params(self, X):
_validate_type(self.n_components, int, "n_components")
# reg is validated in _regularized_covariance
if self.signal_cov is not None:
if isinstance(self.signal_cov, Covariance):
self.signal_cov = self.signal_cov.data
elif not isinstance(self.signal_cov, np.ndarray):
raise ValueError("signal_cov should be mne.Covariance or np.ndarray")
if not np.array_equal(self.signal_cov.shape, np.tile(X.shape[1], 2)):
raise ValueError(
"signal_cov data should be of shape (n_channels, n_channels)"
)
_validate_type(self.cov_method_params, (abc.Mapping, None), "cov_method_params")
_validate_type(self.info, (Info, None), "info")
def fit(self, X, y=None):
"""Fit Xdawn spatial filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_samples)
The target data.
y : array, shape (n_epochs,) | None
The target labels. If None, Xdawn fit on the average evoked.
Returns
-------
self : Xdawn instance
The Xdawn instance.
"""
X, y = self._check_data(X, y=y, fit=True, return_y=True)
# For test purposes
if y is None:
y = np.ones(len(X))
self._validate_params(X)
super().fit(X, y)
return self
def transform(self, X):
"""Transform data with spatial filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_samples)
The target data.
Returns
-------
X : array, shape (n_epochs, n_components * n_classes, n_samples)
The transformed data.
"""
X = self._check_data(X)
X = super().transform(X)
return X
def inverse_transform(self, X):
"""Remove selected components from the signal.
Given the unmixing matrix, transform data, zero out components,
and inverse transform the data. This procedure will reconstruct
the signals from which the dynamics described by the excluded
components is subtracted.
Parameters
----------
X : array, shape (n_epochs, n_components * n_classes, n_times)
The transformed data.
Returns
-------
X : array, shape (n_epochs, n_channels * n_classes, n_times)
The inverse transform data.
"""
# Check size
X = self._check_data(X, check_n_features=False)
n_epochs, n_comp, n_times = X.shape
if n_comp != (self.n_components * len(self.classes_)):
raise ValueError(
f"X must have {self.n_components * len(self.classes_)} components, "
f"got {n_comp} instead."
)
pick_patterns = self._subset_multi_components(name="patterns")
# Transform
return np.dot(pick_patterns.T, X).transpose(1, 0, 2)
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