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csp.py
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csp.py
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# -*- coding: utf-8 -*-
# Authors: Romain Trachel <trachelr@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Alexandre Barachant <alexandre.barachant@gmail.com>
# Clemens Brunner <clemens.brunner@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import copy as cp
import numpy as np
from scipy import linalg
from .mixin import TransformerMixin
from .base import BaseEstimator
from ..cov import _regularized_covariance
from ..utils import fill_doc, _check_option
@fill_doc
class CSP(TransformerMixin, BaseEstimator):
u"""M/EEG signal decomposition using the Common Spatial Patterns (CSP).
This object can be used as a supervised decomposition to estimate
spatial filters for feature extraction in a 2 class decoding problem.
CSP in the context of EEG was first described in [1]; a comprehensive
tutorial on CSP can be found in [2]. Multiclass solving is implemented
from [3].
Parameters
----------
n_components : int, default 4
The number of components to decompose M/EEG signals.
This number should be set by cross-validation.
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`.
log : None | bool (default None)
If transform_into == 'average_power' and log is None or True, then
applies a log transform to standardize the features, else the features
are z-scored. If transform_into == 'csp_space', then log must be None.
cov_est : 'concat' | 'epoch', default 'concat'
If 'concat', covariance matrices are estimated on concatenated epochs
for each class.
If 'epoch', covariance matrices are estimated on each epoch separately
and then averaged over each class.
transform_into : {'average_power', 'csp_space'}
If 'average_power' then self.transform will return the average power of
each spatial filter. If 'csp_space' self.transform will return the data
in CSP space. Defaults to 'average_power'.
norm_trace : bool
Normalize class covariance by its trace. Defaults to False. Trace
normalization is a step of the original CSP algorithm [1]_ to eliminate
magnitude variations in the EEG between individuals. It is not applied
in more recent work [2]_, [3]_ and can have a negative impact on
patterns ordering.
cov_method_params : dict | None
Parameters to pass to :func:`mne.compute_covariance`.
.. versionadded:: 0.16
%(rank_None)s
.. versionadded:: 0.17
Attributes
----------
filters_ : ndarray, shape (n_channels, n_channels)
If fit, the CSP components used to decompose the data, else None.
patterns_ : ndarray, shape (n_channels, n_channels)
If fit, the CSP patterns used to restore M/EEG signals, else None.
mean_ : ndarray, shape (n_components,)
If fit, the mean squared power for each component.
std_ : ndarray, shape (n_components,)
If fit, the std squared power for each component.
See Also
--------
mne.preprocessing.Xdawn, SPoC
References
----------
.. [1] Zoltan J. Koles, Michael S. Lazar, Steven Z. Zhou. Spatial Patterns
Underlying Population Differences in the Background EEG. Brain
Topography 2(4), 275-284, 1990.
.. [2] Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe,
Klaus-Robert Müller. Optimizing Spatial Filters for Robust EEG
Single-Trial Analysis. IEEE Signal Processing Magazine 25(1), 41-56,
2008.
.. [3] Grosse-Wentrup, Moritz, and Martin Buss. Multiclass common spatial
patterns and information theoretic feature extraction. IEEE
Transactions on Biomedical Engineering, Vol 55, no. 8, 2008.
"""
def __init__(self, n_components=4, reg=None, log=None, cov_est="concat",
transform_into='average_power', norm_trace=False,
cov_method_params=None, rank=None):
"""Init of CSP."""
# Init default CSP
if not isinstance(n_components, int):
raise ValueError('n_components must be an integer.')
self.n_components = n_components
self.rank = rank
self.reg = reg
# Init default cov_est
if not (cov_est == "concat" or cov_est == "epoch"):
raise ValueError("unknown covariance estimation method")
self.cov_est = cov_est
# Init default transform_into
_check_option('transform_into', transform_into,
['average_power', 'csp_space'])
self.transform_into = transform_into
# Init default log
if transform_into == 'average_power':
if log is not None and not isinstance(log, bool):
raise ValueError('log must be a boolean if transform_into == '
'"average_power".')
else:
if log is not None:
raise ValueError('log must be a None if transform_into == '
'"csp_space".')
self.log = log
if not isinstance(norm_trace, bool):
raise ValueError('norm_trace must be a bool.')
self.norm_trace = norm_trace
self.cov_method_params = cov_method_params
def _check_Xy(self, X, y=None):
"""Aux. function to check input data."""
if y is not None:
if len(X) != len(y) or len(y) < 1:
raise ValueError('X and y must have the same length.')
if X.ndim < 3:
raise ValueError('X must have at least 3 dimensions.')
def fit(self, X, y):
"""Estimate the CSP decomposition on epochs.
Parameters
----------
X : ndarray, shape (n_epochs, n_channels, n_times)
The data on which to estimate the CSP.
y : array, shape (n_epochs,)
The class for each epoch.
Returns
-------
self : instance of CSP
Returns the modified instance.
"""
if not isinstance(X, np.ndarray):
raise ValueError("X should be of type ndarray (got %s)."
% type(X))
self._check_Xy(X, y)
n_channels = X.shape[1]
self._classes = np.unique(y)
n_classes = len(self._classes)
if n_classes < 2:
raise ValueError("n_classes must be >= 2.")
covs = np.zeros((n_classes, n_channels, n_channels))
sample_weights = list()
for class_idx, this_class in enumerate(self._classes):
if self.cov_est == "concat": # concatenate epochs
class_ = np.transpose(X[y == this_class], [1, 0, 2])
class_ = class_.reshape(n_channels, -1)
cov = _regularized_covariance(
class_, reg=self.reg, method_params=self.cov_method_params,
rank=self.rank)
weight = sum(y == this_class)
elif self.cov_est == "epoch":
class_ = X[y == this_class]
cov = np.zeros((n_channels, n_channels))
for this_X in class_:
cov += _regularized_covariance(
this_X, reg=self.reg,
method_params=self.cov_method_params,
rank=self.rank)
cov /= len(class_)
weight = len(class_)
covs[class_idx] = cov
if self.norm_trace:
# Append covariance matrix and weight. Prior to version 0.15,
# trace normalization was applied, but was breaking results for
# some usecases by changing the apparent ranking of patterns.
# Trace normalization of the covariance matrix was removed
# without signigificant effect on patterns or performances.
# If the user interested in this feature, we suggest trace
# normalization of the epochs prior to the CSP.
covs[class_idx] /= np.trace(cov)
sample_weights.append(weight)
if n_classes == 2:
eigen_values, eigen_vectors = linalg.eigh(covs[0], covs.sum(0))
# sort eigenvectors
ix = np.argsort(np.abs(eigen_values - 0.5))[::-1]
else:
# The multiclass case is adapted from
# http://github.com/alexandrebarachant/pyRiemann
eigen_vectors, D = _ajd_pham(covs)
# Here we apply an euclidean mean. See pyRiemann for other metrics
mean_cov = np.average(covs, axis=0, weights=sample_weights)
eigen_vectors = eigen_vectors.T
# normalize
for ii in range(eigen_vectors.shape[1]):
tmp = np.dot(np.dot(eigen_vectors[:, ii].T, mean_cov),
eigen_vectors[:, ii])
eigen_vectors[:, ii] /= np.sqrt(tmp)
# class probability
class_probas = [np.mean(y == _class) for _class in self._classes]
# mutual information
mutual_info = []
for jj in range(eigen_vectors.shape[1]):
aa, bb = 0, 0
for (cov, prob) in zip(covs, class_probas):
tmp = np.dot(np.dot(eigen_vectors[:, jj].T, cov),
eigen_vectors[:, jj])
aa += prob * np.log(np.sqrt(tmp))
bb += prob * (tmp ** 2 - 1)
mi = - (aa + (3.0 / 16) * (bb ** 2))
mutual_info.append(mi)
ix = np.argsort(mutual_info)[::-1]
# sort eigenvectors
eigen_vectors = eigen_vectors[:, ix]
self.filters_ = eigen_vectors.T
self.patterns_ = linalg.pinv2(eigen_vectors)
pick_filters = self.filters_[:self.n_components]
X = np.asarray([np.dot(pick_filters, epoch) for epoch in X])
# compute features (mean band power)
X = (X ** 2).mean(axis=2)
# To standardize features
self.mean_ = X.mean(axis=0)
self.std_ = X.std(axis=0)
return self
def transform(self, X):
"""Estimate epochs sources given the CSP filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_times)
The data.
Returns
-------
X : ndarray
If self.transform_into == 'average_power' then returns the power of
CSP features averaged over time and shape (n_epochs, n_sources)
If self.transform_into == 'csp_space' then returns the data in CSP
space and shape is (n_epochs, n_sources, n_times)
"""
if not isinstance(X, np.ndarray):
raise ValueError("X should be of type ndarray (got %s)." % type(X))
if self.filters_ is None:
raise RuntimeError('No filters available. Please first fit CSP '
'decomposition.')
pick_filters = self.filters_[:self.n_components]
X = np.asarray([np.dot(pick_filters, epoch) for epoch in X])
# compute features (mean band power)
if self.transform_into == 'average_power':
X = (X ** 2).mean(axis=2)
log = True if self.log is None else self.log
if log:
X = np.log(X)
else:
X -= self.mean_
X /= self.std_
return X
def plot_patterns(self, info, components=None, ch_type=None, layout=None,
vmin=None, vmax=None, cmap='RdBu_r', sensors=True,
colorbar=True, scalings=None, units='a.u.', res=64,
size=1, cbar_fmt='%3.1f', name_format='CSP%01d',
show=True, show_names=False, title=None, mask=None,
mask_params=None, outlines='head', contours=6,
image_interp='bilinear', average=None, head_pos=None):
"""Plot topographic patterns of components.
The patterns explain how the measured data was generated from the
neural sources (a.k.a. the forward model).
Parameters
----------
info : instance of Info
Info dictionary of the epochs used for fitting.
If not possible, consider using ``create_info``.
components : float | array of float | None.
The patterns to plot. If None, n_components will be shown.
ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
The channel type to plot. For 'grad', the gradiometers are
collected in pairs and the RMS for each pair is plotted.
If None, then first available channel type from order given
above is used. Defaults to None.
layout : None | Layout
Layout instance specifying sensor positions (does not need to be
specified for Neuromag data). If possible, the correct layout file
is inferred from the data; if no appropriate layout file was found
the layout is automatically generated from the sensor locations.
vmin : float | callable
The value specifying the lower bound of the color range.
If None, and vmax is None, -vmax is used. Else np.min(data).
If callable, the output equals vmin(data).
vmax : float | callable
The value specifying the upper bound of the color range.
If None, the maximum absolute value is used. If vmin is None,
but vmax is not, default np.min(data).
If callable, the output equals vmax(data).
cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None
Colormap to use. If tuple, the first value indicates the colormap
to use and the second value is a boolean defining interactivity. In
interactive mode the colors are adjustable by clicking and dragging
the colorbar with left and right mouse button. Left mouse button
moves the scale up and down and right mouse button adjusts the
range. Hitting space bar resets the range. Up and down arrows can
be used to change the colormap. If None, 'Reds' is used for all
positive data, otherwise defaults to 'RdBu_r'. If 'interactive',
translates to (None, True). Defaults to 'RdBu_r'.
.. warning:: Interactive mode works smoothly only for a small
amount of topomaps.
sensors : bool | str
Add markers for sensor locations to the plot. Accepts matplotlib
plot format string (e.g., 'r+' for red plusses). If True,
a circle will be used (via .add_artist). Defaults to True.
colorbar : bool
Plot a colorbar.
scalings : dict | float | None
The scalings of the channel types to be applied for plotting.
If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``.
units : dict | str | None
The unit of the channel type used for colorbar label. If
scale is None the unit is automatically determined.
res : int
The resolution of the topomap image (n pixels along each side).
size : float
Side length per topomap in inches.
cbar_fmt : str
String format for colorbar values.
name_format : str
String format for topomap values. Defaults to "CSP%01d"
show : bool
Show figure if True.
show_names : bool | callable
If True, show channel names on top of the map. If a callable is
passed, channel names will be formatted using the callable; e.g.,
to delete the prefix 'MEG ' from all channel names, pass the
function lambda x: x.replace('MEG ', ''). If `mask` is not None,
only significant sensors will be shown.
title : str | None
Title. If None (default), no title is displayed.
mask : ndarray of bool, shape (n_channels, n_times) | None
The channels to be marked as significant at a given time point.
Indices set to `True` will be considered. Defaults to None.
mask_params : dict | None
Additional plotting parameters for plotting significant sensors.
Default (None) equals::
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
outlines : 'head' | 'skirt' | dict | None
The outlines to be drawn. If 'head', the default head scheme will
be drawn. If 'skirt' the head scheme will be drawn, but sensors are
allowed to be plotted outside of the head circle. If dict, each key
refers to a tuple of x and y positions, the values in 'mask_pos'
will serve as image mask, and the 'autoshrink' (bool) field will
trigger automated shrinking of the positions due to points outside
the outline. Alternatively, a matplotlib patch object can be passed
for advanced masking options, either directly or as a function that
returns patches (required for multi-axis plots). If None, nothing
will be drawn. Defaults to 'head'.
contours : int | array of float
The number of contour lines to draw. If 0, no contours will be
drawn. When an integer, matplotlib ticker locator is used to find
suitable values for the contour thresholds (may sometimes be
inaccurate, use array for accuracy). If an array, the values
represent the levels for the contours. Defaults to 6.
image_interp : str
The image interpolation to be used.
All matplotlib options are accepted.
average : float | None
The time window around a given time to be used for averaging
(seconds). For example, 0.01 would translate into window that
starts 5 ms before and ends 5 ms after a given time point.
Defaults to None, which means no averaging.
head_pos : dict | None
If None (default), the sensors are positioned such that they span
the head circle. If dict, can have entries 'center' (tuple) and
'scale' (tuple) for what the center and scale of the head
should be relative to the electrode locations.
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure.
"""
from .. import EvokedArray
if components is None:
components = np.arange(self.n_components)
# set sampling frequency to have 1 component per time point
info = cp.deepcopy(info)
info['sfreq'] = 1.
# create an evoked
patterns = EvokedArray(self.patterns_.T, info, tmin=0)
# the call plot_topomap
return patterns.plot_topomap(
times=components, ch_type=ch_type, layout=layout,
vmin=vmin, vmax=vmax, cmap=cmap, colorbar=colorbar, res=res,
cbar_fmt=cbar_fmt, sensors=sensors,
scalings=scalings, units=units, time_unit='s',
time_format=name_format, size=size, show_names=show_names,
title=title, mask_params=mask_params, mask=mask, outlines=outlines,
contours=contours, image_interp=image_interp, show=show,
average=average, head_pos=head_pos)
def plot_filters(self, info, components=None, ch_type=None, layout=None,
vmin=None, vmax=None, cmap='RdBu_r', sensors=True,
colorbar=True, scalings=None, units='a.u.', res=64,
size=1, cbar_fmt='%3.1f', name_format='CSP%01d',
show=True, show_names=False, title=None, mask=None,
mask_params=None, outlines='head', contours=6,
image_interp='bilinear', average=None, head_pos=None):
"""Plot topographic filters of components.
The filters are used to extract discriminant neural sources from
the measured data (a.k.a. the backward model).
Parameters
----------
info : instance of Info
Info dictionary of the epochs used for fitting.
If not possible, consider using ``create_info``.
components : float | array of float | None
The patterns to plot. If None, n_components will be shown.
ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
The channel type to plot. For 'grad', the gradiometers are
collected in pairs and the RMS for each pair is plotted.
If None, then first available channel type from order given
above is used. Defaults to None.
layout : None | Layout
Layout instance specifying sensor positions (does not need to be
specified for Neuromag data). If possible, the correct layout file
is inferred from the data; if no appropriate layout file was found
the layout is automatically generated from the sensor locations.
vmin : float | callable
The value specifying the lower bound of the color range.
If None, and vmax is None, -vmax is used. Else np.min(data).
If callable, the output equals vmin(data).
vmax : float | callable
The value specifying the upper bound of the color range.
If None, the maximum absolute value is used. If vmin is None,
but vmax is not, defaults to np.min(data).
If callable, the output equals vmax(data).
cmap : matplotlib colormap | (colormap, bool) | 'interactive' | None
Colormap to use. If tuple, the first value indicates the colormap
to use and the second value is a boolean defining interactivity. In
interactive mode the colors are adjustable by clicking and dragging
the colorbar with left and right mouse button. Left mouse button
moves the scale up and down and right mouse button adjusts the
range. Hitting space bar resets the range. Up and down arrows can
be used to change the colormap. If None, 'Reds' is used for all
positive data, otherwise defaults to 'RdBu_r'. If 'interactive',
translates to (None, True). Defaults to 'RdBu_r'.
.. warning:: Interactive mode works smoothly only for a small
amount of topomaps.
sensors : bool | str
Add markers for sensor locations to the plot. Accepts matplotlib
plot format string (e.g., 'r+' for red plusses). If True,
a circle will be used (via .add_artist). Defaults to True.
colorbar : bool
Plot a colorbar.
scalings : dict | float | None
The scalings of the channel types to be applied for plotting.
If None, defaults to ``dict(eeg=1e6, grad=1e13, mag=1e15)``.
units : dict | str | None
The unit of the channel type used for colorbar label. If
scale is None the unit is automatically determined.
res : int
The resolution of the topomap image (n pixels along each side).
size : float
Side length per topomap in inches.
cbar_fmt : str
String format for colorbar values.
name_format : str
String format for topomap values. Defaults to "CSP%01d"
show : bool
Show figure if True.
show_names : bool | callable
If True, show channel names on top of the map. If a callable is
passed, channel names will be formatted using the callable; e.g.,
to delete the prefix 'MEG ' from all channel names, pass the
function lambda x: x.replace('MEG ', ''). If `mask` is not None,
only significant sensors will be shown.
title : str | None
Title. If None (default), no title is displayed.
mask : ndarray of bool, shape (n_channels, n_times) | None
The channels to be marked as significant at a given time point.
Indices set to `True` will be considered. Defaults to None.
mask_params : dict | None
Additional plotting parameters for plotting significant sensors.
Default (None) equals::
dict(marker='o', markerfacecolor='w', markeredgecolor='k',
linewidth=0, markersize=4)
outlines : 'head' | 'skirt' | dict | None
The outlines to be drawn. If 'head', the default head scheme will
be drawn. If 'skirt' the head scheme will be drawn, but sensors are
allowed to be plotted outside of the head circle. If dict, each key
refers to a tuple of x and y positions, the values in 'mask_pos'
will serve as image mask, and the 'autoshrink' (bool) field will
trigger automated shrinking of the positions due to points outside
the outline. Alternatively, a matplotlib patch object can be passed
for advanced masking options, either directly or as a function that
returns patches (required for multi-axis plots). If None, nothing
will be drawn. Defaults to 'head'.
contours : int | array of float
The number of contour lines to draw. If 0, no contours will be
drawn. When an integer, matplotlib ticker locator is used to find
suitable values for the contour thresholds (may sometimes be
inaccurate, use array for accuracy). If an array, the values
represent the levels for the contours. Defaults to 6.
image_interp : str
The image interpolation to be used.
All matplotlib options are accepted.
average : float | None
The time window around a given time to be used for averaging
(seconds). For example, 0.01 would translate into window that
starts 5 ms before and ends 5 ms after a given time point.
Defaults to None, which means no averaging.
head_pos : dict | None
If None (default), the sensors are positioned such that they span
the head circle. If dict, can have entries 'center' (tuple) and
'scale' (tuple) for what the center and scale of the head
should be relative to the electrode locations.
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure.
"""
from .. import EvokedArray
if components is None:
components = np.arange(self.n_components)
# set sampling frequency to have 1 component per time point
info = cp.deepcopy(info)
info['sfreq'] = 1.
# create an evoked
filters = EvokedArray(self.filters_, info, tmin=0)
# the call plot_topomap
return filters.plot_topomap(
times=components, ch_type=ch_type, layout=layout, vmin=vmin,
vmax=vmax, cmap=cmap, colorbar=colorbar, res=res,
cbar_fmt=cbar_fmt, sensors=sensors, scalings=scalings, units=units,
time_unit='s', time_format=name_format, size=size,
show_names=show_names, title=title, mask_params=mask_params,
mask=mask, outlines=outlines, contours=contours,
image_interp=image_interp, show=show, average=average,
head_pos=head_pos)
def _ajd_pham(X, eps=1e-6, max_iter=15):
"""Approximate joint diagonalization based on Pham's algorithm.
This is a direct implementation of the PHAM's AJD algorithm [1].
Parameters
----------
X : ndarray, shape (n_epochs, n_channels, n_channels)
A set of covariance matrices to diagonalize.
eps : float, default 1e-6
The tolerance for stopping criterion.
max_iter : int, default 1000
The maximum number of iteration to reach convergence.
Returns
-------
V : ndarray, shape (n_channels, n_channels)
The diagonalizer.
D : ndarray, shape (n_epochs, n_channels, n_channels)
The set of quasi diagonal matrices.
References
----------
.. [1] Pham, Dinh Tuan. "Joint approximate diagonalization of positive
definite Hermitian matrices." SIAM Journal on Matrix Analysis and
Applications 22, no. 4 (2001): 1136-1152.
"""
# Adapted from http://github.com/alexandrebarachant/pyRiemann
n_epochs = X.shape[0]
# Reshape input matrix
A = np.concatenate(X, axis=0).T
# Init variables
n_times, n_m = A.shape
V = np.eye(n_times)
epsilon = n_times * (n_times - 1) * eps
for it in range(max_iter):
decr = 0
for ii in range(1, n_times):
for jj in range(ii):
Ii = np.arange(ii, n_m, n_times)
Ij = np.arange(jj, n_m, n_times)
c1 = A[ii, Ii]
c2 = A[jj, Ij]
g12 = np.mean(A[ii, Ij] / c1)
g21 = np.mean(A[ii, Ij] / c2)
omega21 = np.mean(c1 / c2)
omega12 = np.mean(c2 / c1)
omega = np.sqrt(omega12 * omega21)
tmp = np.sqrt(omega21 / omega12)
tmp1 = (tmp * g12 + g21) / (omega + 1)
tmp2 = (tmp * g12 - g21) / max(omega - 1, 1e-9)
h12 = tmp1 + tmp2
h21 = np.conj((tmp1 - tmp2) / tmp)
decr += n_epochs * (g12 * np.conj(h12) + g21 * h21) / 2.0
tmp = 1 + 1.j * 0.5 * np.imag(h12 * h21)
tmp = np.real(tmp + np.sqrt(tmp ** 2 - h12 * h21))
tau = np.array([[1, -h12 / tmp], [-h21 / tmp, 1]])
A[[ii, jj], :] = np.dot(tau, A[[ii, jj], :])
tmp = np.c_[A[:, Ii], A[:, Ij]]
tmp = np.reshape(tmp, (n_times * n_epochs, 2), order='F')
tmp = np.dot(tmp, tau.T)
tmp = np.reshape(tmp, (n_times, n_epochs * 2), order='F')
A[:, Ii] = tmp[:, :n_epochs]
A[:, Ij] = tmp[:, n_epochs:]
V[[ii, jj], :] = np.dot(tau, V[[ii, jj], :])
if decr < epsilon:
break
D = np.reshape(A, (n_times, -1, n_times)).transpose(1, 0, 2)
return V, D
@fill_doc
class SPoC(CSP):
"""Implementation of the SPoC spatial filtering.
Source Power Comodulation (SPoC) [1]_ allows to extract spatial filters and
patterns by using a target (continuous) variable in the decomposition
process in order to give preference to components whose power correlates
with the target variable.
SPoC can be seen as an extension of the CSP driven by a continuous
variable rather than a discrete variable. Typical applications include
extraction of motor patterns using EMG power or audio patterns using sound
envelope.
Parameters
----------
n_components : int
The number of components to decompose M/EEG 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`.
log : None | bool (default None)
If transform_into == 'average_power' and log is None or True, then
applies a log transform to standardize the features, else the features
are z-scored. If transform_into == 'csp_space', then log must be None.
transform_into : {'average_power', 'csp_space'}
If 'average_power' then self.transform will return the average power of
each spatial filter. If 'csp_space' self.transform will return the data
in CSP space. Defaults to 'average_power'.
cov_method_params : dict | None
Parameters to pass to :func:`mne.compute_covariance`.
.. versionadded:: 0.16
%(rank_None)s
.. versionadded:: 0.17
Attributes
----------
filters_ : ndarray, shape (n_channels, n_channels)
If fit, the SPoC spatial filters, else None.
patterns_ : ndarray, shape (n_channels, n_channels)
If fit, the SPoC spatial patterns, else None.
mean_ : ndarray, shape (n_components,)
If fit, the mean squared power for each component.
std_ : ndarray, shape (n_components,)
If fit, the std squared power for each component.
See Also
--------
mne.preprocessing.Xdawn, CSP
References
----------
.. [1] Dahne, S., Meinecke, F. C., Haufe, S., Hohne, J., Tangermann, M.,
Muller, K. R., & Nikulin, V. V. (2014). SPoC: a novel framework for
relating the amplitude of neuronal oscillations to behaviorally
relevant parameters. NeuroImage, 86, 111-122.
"""
def __init__(self, n_components=4, reg=None, log=None,
transform_into='average_power', cov_method_params=None,
rank=None):
"""Init of SPoC."""
super(SPoC, self).__init__(n_components=n_components, reg=reg, log=log,
cov_est="epoch", norm_trace=False,
transform_into=transform_into, rank=rank,
cov_method_params=cov_method_params)
# Covariance estimation have to be done on the single epoch level,
# unlike CSP where covariance estimation can also be achieved through
# concatenation of all epochs from the same class.
delattr(self, 'cov_est')
delattr(self, 'norm_trace')
def fit(self, X, y):
"""Estimate the SPoC decomposition on epochs.
Parameters
----------
X : ndarray, shape (n_epochs, n_channels, n_times)
The data on which to estimate the SPoC.
y : array, shape (n_epochs,)
The class for each epoch.
Returns
-------
self : instance of SPoC
Returns the modified instance.
"""
if not isinstance(X, np.ndarray):
raise ValueError("X should be of type ndarray (got %s)."
% type(X))
self._check_Xy(X, y)
if len(np.unique(y)) < 2:
raise ValueError("y must have at least two distinct values.")
# The following code is directly copied from pyRiemann
# Normalize target variable
target = y.astype(np.float64)
target -= target.mean()
target /= target.std()
n_epochs, n_channels = X.shape[:2]
# Estimate single trial covariance
covs = np.empty((n_epochs, n_channels, n_channels))
for ii, epoch in enumerate(X):
covs[ii] = _regularized_covariance(
epoch, reg=self.reg, method_params=self.cov_method_params,
rank=self.rank)
C = covs.mean(0)
Cz = np.mean(covs * target[:, np.newaxis, np.newaxis], axis=0)
# solve eigenvalue decomposition
evals, evecs = linalg.eigh(Cz, C)
evals = evals.real
evecs = evecs.real
# sort vectors
ix = np.argsort(np.abs(evals))[::-1]
# sort eigenvectors
evecs = evecs[:, ix].T
# spatial patterns
self.patterns_ = linalg.pinv(evecs).T # n_channels x n_channels
self.filters_ = evecs # n_channels x n_channels
pick_filters = self.filters_[:self.n_components]
X = np.asarray([np.dot(pick_filters, epoch) for epoch in X])
# compute features (mean band power)
X = (X ** 2).mean(axis=-1)
# To standardize features
self.mean_ = X.mean(axis=0)
self.std_ = X.std(axis=0)
return self
def transform(self, X):
"""Estimate epochs sources given the SPoC filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_times)
The data.
Returns
-------
X : ndarray
If self.transform_into == 'average_power' then returns the power of
CSP features averaged over time and shape (n_epochs, n_sources)
If self.transform_into == 'csp_space' then returns the data in CSP
space and shape is (n_epochs, n_sources, n_times)
"""
return super(SPoC, self).transform(X)