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ssd.py
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ssd.py
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# Author: Denis A. Engemann <denis.engemann@gmail.com>
# Victoria Peterson <victoriapeterson09@gmail.com>
# License: BSD (3-clause)
import numpy as np
from scipy.linalg import eigh
from ..filter import filter_data
from ..cov import _regularized_covariance
from . import TransformerMixin, BaseEstimator
from ..time_frequency import psd_array_welch
from ..utils import _time_mask, fill_doc, _validate_type, _check_option
from ..io.pick import _get_channel_types, _picks_to_idx
@fill_doc
class SSD(BaseEstimator, TransformerMixin):
"""
M/EEG signal decomposition using the Spatio-Spectral Decomposition (SSD).
SSD seeks to maximize the power at a frequency band of interest while
simultaneously minimizing it at the flanking (surrounding) frequency bins
(considered noise). It extremizes the covariance matrices associated with
signal and noise :footcite:`NikulinEtAl2011`.
SSD can either be used as a dimensionality reduction method or a
‘denoised’ low rank factorization method :footcite:`HaufeEtAl2014b`.
Parameters
----------
info : instance of mne.Info
The info object containing the channel and sampling information.
It must match the input data.
filt_params_signal : dict
Filtering for the frequencies of interest.
filt_params_noise : dict
Filtering for the frequencies of non-interest.
reg : float | str | None (default)
Which covariance estimator to use.
If not None (same as 'empirical'), 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`.
n_components : int | None (default None)
The number of components to extract from the signal.
If n_components is None, no dimensionality reduction is applied.
picks : array of int | None (default None)
The indices of good channels.
sort_by_spectral_ratio : bool (default False)
If set to True, the components are sorted accordingly
to the spectral ratio.
See Eq. (24) in :footcite:`NikulinEtAl2011`.
return_filtered : bool (default True)
If return_filtered is True, data is bandpassed and projected onto
the SSD components.
n_fft : int (default None)
If sort_by_spectral_ratio is set to True, then the SSD sources will be
sorted accordingly to their spectral ratio which is calculated based on
:func:`mne.time_frequency.psd_array_welch` function. The n_fft parameter
set the length of FFT used.
See :func:`mne.time_frequency.psd_array_welch` for more information.
cov_method_params : dict | None (default None)
As in :class:`mne.decoding.SPoC`
The default is None.
rank : None | dict | ‘info’ | ‘full’
As in :class:`mne.decoding.SPoC`
This controls the rank computation that can be read from the
measurement info or estimated from the data.
See Notes of :func:`mne.compute_rank` for details.
We recommend to use 'full' when working with epoched data.
Attributes
----------
filters_ : array, shape (n_channels, n_components)
The spatial filters to be multiplied with the signal.
patterns_ : array, shape (n_components, n_channels)
The patterns for reconstructing the signal from the filtered data.
References
----------
.. footbibliography::
"""
def __init__(self, info, filt_params_signal, filt_params_noise,
reg=None, n_components=None, picks=None,
sort_by_spectral_ratio=True, return_filtered=False,
n_fft=None, cov_method_params=None, rank=None):
"""Initialize instance."""
dicts = {"signal": filt_params_signal, "noise": filt_params_noise}
for param, dd in [('l', 0), ('h', 0), ('l', 1), ('h', 1)]:
key = ('signal', 'noise')[dd]
if param + '_freq' not in dicts[key]:
raise ValueError(
'%s must be defined in filter parameters for %s'
% (param + '_freq', key))
val = dicts[key][param + '_freq']
if not isinstance(val, (int, float)):
_validate_type(val, ('numeric',), f'{key} {param}_freq')
# check freq bands
if (filt_params_noise['l_freq'] > filt_params_signal['l_freq'] or
filt_params_signal['h_freq'] > filt_params_noise['h_freq']):
raise ValueError('Wrongly specified frequency bands!\n'
'The signal band-pass must be within the noise '
'band-pass!')
self.picks_ = _picks_to_idx(info, picks, none='data', exclude='bads')
del picks
ch_types = _get_channel_types(info, picks=self.picks_, unique=True)
if len(ch_types) > 1:
raise ValueError('At this point SSD only supports fitting '
'single channel types. Your info has %i types' %
(len(ch_types)))
self.info = info
self.freqs_signal = (filt_params_signal['l_freq'],
filt_params_signal['h_freq'])
self.freqs_noise = (filt_params_noise['l_freq'],
filt_params_noise['h_freq'])
self.filt_params_signal = filt_params_signal
self.filt_params_noise = filt_params_noise
self.sort_by_spectral_ratio = sort_by_spectral_ratio
if n_fft is None:
self.n_fft = int(self.info['sfreq'])
else:
self.n_fft = int(n_fft)
self.return_filtered = return_filtered
self.reg = reg
self.n_components = n_components
self.rank = rank
self.cov_method_params = cov_method_params
def _check_X(self, X):
"""Check input data."""
_validate_type(X, np.ndarray, 'X')
_check_option('X.ndim', X.ndim, (2, 3))
n_chan = X.shape[-2]
if n_chan != self.info['nchan']:
raise ValueError('Info must match the input data.'
'Found %i channels but expected %i.' %
(n_chan, self.info['nchan']))
def fit(self, X, y=None):
"""Estimate the SSD decomposition on raw or epoched data.
Parameters
----------
X : array, shape ([n_epochs, ]n_channels, n_times)
The input data from which to estimate the SSD. Either 2D array
obtained from continuous data or 3D array obtained from epoched
data.
y : None | array, shape (n_samples,)
Used for scikit-learn compatibility.
Returns
-------
self : instance of SSD
Returns the modified instance.
"""
self._check_X(X)
X_aux = X[..., self.picks_, :]
X_signal = filter_data(
X_aux, self.info['sfreq'], **self.filt_params_signal)
X_noise = filter_data(
X_aux, self.info['sfreq'], **self.filt_params_noise)
X_noise -= X_signal
if X.ndim == 3:
X_signal = np.hstack(X_signal)
X_noise = np.hstack(X_noise)
cov_signal = _regularized_covariance(
X_signal, reg=self.reg, method_params=self.cov_method_params,
rank=self.rank, info=self.info)
cov_noise = _regularized_covariance(
X_noise, reg=self.reg, method_params=self.cov_method_params,
rank=self.rank, info=self.info)
eigvals_, eigvects_ = eigh(cov_signal, cov_noise)
# sort in descending order
ix = np.argsort(eigvals_)[::-1]
self.eigvals_ = eigvals_[ix]
self.filters_ = eigvects_[:, ix]
self.patterns_ = np.linalg.pinv(self.filters_)
return self
def transform(self, X):
"""Estimate epochs sources given the SSD filters.
Parameters
----------
X : array, shape ([n_epochs, ]n_channels, n_times)
The input data from which to estimate the SSD. Either 2D array
obtained from continuous data or 3D array obtained from epoched
data.
Returns
-------
X_ssd : array, shape ([n_epochs, ]n_components, n_times)
The processed data.
"""
self._check_X(X)
if self.filters_ is None:
raise RuntimeError('No filters available. Please first call fit')
X_ssd = self.filters_.T @ X[..., self.picks_, :]
# We assume that ordering by spectral ratio is more important
# than the initial ordering. This is why we apply component picks
# after ordering.
sorter_spec = Ellipsis
if self.sort_by_spectral_ratio:
_, sorter_spec = self.get_spectral_ratio(ssd_sources=X_ssd)
if X.ndim == 2:
X_ssd = X_ssd[sorter_spec][:self.n_components]
else:
X_ssd = X_ssd[:, sorter_spec, :][:, :self.n_components, :]
return X_ssd
def get_spectral_ratio(self, ssd_sources):
"""Get the spectal signal-to-noise ratio for each spatial filter.
Spectral ratio measure for best n_components selection
See :footcite:`NikulinEtAl2011`, Eq. (24).
Parameters
----------
ssd_sources : array
Data projectded to SSD space.
Returns
-------
spec_ratio : array, shape (n_channels)
Array with the sprectal ratio value for each component.
sorter_spec : array, shape (n_channels)
Array of indices for sorting spec_ratio.
References
----------
.. footbibliography::
"""
psd, freqs = psd_array_welch(
ssd_sources, sfreq=self.info['sfreq'], n_fft=self.n_fft)
sig_idx = _time_mask(freqs, *self.freqs_signal)
noise_idx = _time_mask(freqs, *self.freqs_noise)
if psd.ndim == 3:
mean_sig = psd[:, :, sig_idx].mean(axis=2).mean(axis=0)
mean_noise = psd[:, :, noise_idx].mean(axis=2).mean(axis=0)
spec_ratio = mean_sig / mean_noise
else:
mean_sig = psd[:, sig_idx].mean(axis=1)
mean_noise = psd[:, noise_idx].mean(axis=1)
spec_ratio = mean_sig / mean_noise
sorter_spec = spec_ratio.argsort()[::-1]
return spec_ratio, sorter_spec
def inverse_transform(self):
"""Not implemented yet."""
raise NotImplementedError('inverse_transform is not yet available.')
def apply(self, X):
"""Remove selected components from the signal.
This procedure will reconstruct M/EEG signals from which the dynamics
described by the excluded components is subtracted
(denoised by low-rank factorization).
See :footcite:`HaufeEtAl2014b` for more information.
.. note:: Unlike in other classes with an apply method,
only NumPy arrays are supported (not instances of MNE objects).
Parameters
----------
X : array, shape ([n_epochs, ]n_channels, n_times)
The input data from which to estimate the SSD. Either 2D array
obtained from continuous data or 3D array obtained from epoched
data.
Returns
-------
X : array, shape ([n_epochs, ]n_channels, n_times)
The processed data.
"""
X_ssd = self.transform(X)
sorter_spec = Ellipsis
if self.sort_by_spectral_ratio:
_, sorter_spec = self.get_spectral_ratio(ssd_sources=X_ssd)
pick_patterns = self.patterns_[sorter_spec, :self.n_components].T
X = pick_patterns @ X_ssd
return X