/
time_frequency.py
772 lines (660 loc) · 29.4 KB
/
time_frequency.py
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import numpy as np
from scipy import linalg
from ..epochs import Epochs, make_fixed_length_events
from ..evoked import EvokedArray
from ..io.constants import FIFF
from ..io.pick import pick_info
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper import (_psd_from_mt, _compute_mt_params,
_psd_from_mt_adaptive, _mt_spectra)
from ..baseline import rescale, _log_rescale
from .inverse import (combine_xyz, _check_or_prepare, _assemble_kernel,
_pick_channels_inverse_operator, INVERSE_METHODS,
_check_ori, _subject_from_inverse)
from ..parallel import parallel_func
from ..utils import logger, verbose, ProgressBar, _check_option
def _prepare_source_params(inst, inverse_operator, label=None,
lambda2=1.0 / 9.0, method="dSPM", nave=1,
decim=1, pca=True, pick_ori="normal",
prepared=False, method_params=None, verbose=None):
"""Prepare inverse operator and params for spectral / TFR analysis."""
inv = _check_or_prepare(inverse_operator, nave, lambda2, method,
method_params, prepared)
#
# Pick the correct channels from the data
#
sel = _pick_channels_inverse_operator(inst.ch_names, inv)
logger.info('Picked %d channels from the data' % len(sel))
logger.info('Computing inverse...')
#
# Simple matrix multiplication followed by combination of the
# three current components
#
# This does all the data transformations to compute the weights for the
# eigenleads
#
K, noise_norm, vertno, _ = _assemble_kernel(inv, label, method, pick_ori)
if pca:
U, s, Vh = linalg.svd(K, full_matrices=False)
rank = np.sum(s > 1e-8 * s[0])
K = s[:rank] * U[:, :rank]
Vh = Vh[:rank]
logger.info('Reducing data rank %d -> %d' % (len(s), rank))
else:
Vh = None
is_free_ori = inverse_operator['source_ori'] == FIFF.FIFFV_MNE_FREE_ORI
return K, sel, Vh, vertno, is_free_ori, noise_norm
@verbose
def source_band_induced_power(epochs, inverse_operator, bands, label=None,
lambda2=1.0 / 9.0, method="dSPM", nave=1,
n_cycles=5, df=1, use_fft=False, decim=1,
baseline=None, baseline_mode='logratio',
pca=True, n_jobs=1, prepared=False,
method_params=None, verbose=None):
"""Compute source space induced power in given frequency bands.
Parameters
----------
epochs : instance of Epochs
The epochs.
inverse_operator : instance of InverseOperator
The inverse operator.
bands : dict
Example : bands = dict(alpha=[8, 9]).
label : Label
Restricts the source estimates to a given label.
lambda2 : float
The regularization parameter of the minimum norm.
method : "MNE" | "dSPM" | "sLORETA" | "eLORETA"
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
nave : int
The number of averages used to scale the noise covariance matrix.
n_cycles : float | array of float
Number of cycles. Fixed number or one per frequency.
df : float
delta frequency within bands.
use_fft : bool
Do convolutions in time or frequency domain with FFT.
decim : int
Temporal decimation factor.
baseline : None (default) or tuple, shape (2,)
The time interval to apply baseline correction. If None do not apply
it. If baseline is (a, b) the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used and if b is None then b
is set to the end of the interval. If baseline is equal to (None, None)
all the time interval is used.
baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'
Perform baseline correction by
- subtracting the mean of baseline values ('mean')
- dividing by the mean of baseline values ('ratio')
- dividing by the mean of baseline values and taking the log
('logratio')
- subtracting the mean of baseline values followed by dividing by
the mean of baseline values ('percent')
- subtracting the mean of baseline values and dividing by the
standard deviation of baseline values ('zscore')
- dividing by the mean of baseline values, taking the log, and
dividing by the standard deviation of log baseline values
('zlogratio')
pca : bool
If True, the true dimension of data is estimated before running
the time-frequency transforms. It reduces the computation times
e.g. with a dataset that was maxfiltered (true dim is 64).
%(n_jobs)s
prepared : bool
If True, do not call :func:`prepare_inverse_operator`.
method_params : dict | None
Additional options for eLORETA. See Notes of :func:`apply_inverse`.
.. versionadded:: 0.16
%(verbose)s
Returns
-------
stcs : dict of SourceEstimate (or VolSourceEstimate)
The estimated source space induced power estimates.
""" # noqa: E501
_check_option('method', method, INVERSE_METHODS)
freqs = np.concatenate([np.arange(band[0], band[1] + df / 2.0, df)
for _, band in bands.items()])
powers, _, vertno = _source_induced_power(
epochs, inverse_operator, freqs, label=label, lambda2=lambda2,
method=method, nave=nave, n_cycles=n_cycles, decim=decim,
use_fft=use_fft, pca=pca, n_jobs=n_jobs, with_plv=False,
prepared=prepared, method_params=method_params)
Fs = epochs.info['sfreq'] # sampling in Hz
stcs = dict()
subject = _subject_from_inverse(inverse_operator)
_log_rescale(baseline, baseline_mode) # for early failure
for name, band in bands.items():
idx = [k for k, f in enumerate(freqs) if band[0] <= f <= band[1]]
# average power in band + mean over epochs
power = np.mean(powers[:, idx, :], axis=1)
# Run baseline correction
power = rescale(power, epochs.times[::decim], baseline, baseline_mode,
copy=False, verbose=False)
tmin = epochs.times[0]
tstep = float(decim) / Fs
stc = _make_stc(power, vertices=vertno, tmin=tmin, tstep=tstep,
subject=subject, src_type=inverse_operator['src'].kind)
stcs[name] = stc
logger.info('[done]')
return stcs
def _prepare_tfr(data, decim, pick_ori, Ws, K, source_ori):
"""Prepare TFR source localization."""
n_times = data[:, :, ::decim].shape[2]
n_freqs = len(Ws)
n_sources = K.shape[0]
is_free_ori = False
if (source_ori == FIFF.FIFFV_MNE_FREE_ORI and pick_ori is None):
is_free_ori = True
n_sources //= 3
shape = (n_sources, n_freqs, n_times)
return shape, is_free_ori
@verbose
def _compute_pow_plv(data, K, sel, Ws, source_ori, use_fft, Vh,
with_power, with_plv, pick_ori, decim, verbose=None):
"""Aux function for induced power and PLV."""
shape, is_free_ori = _prepare_tfr(data, decim, pick_ori, Ws, K, source_ori)
power = np.zeros(shape, dtype=np.float) # power or raw TFR
# phase lock
plv = np.zeros(shape, dtype=np.complex) if with_plv else None
for epoch in data:
epoch = epoch[sel] # keep only selected channels
if Vh is not None:
epoch = np.dot(Vh, epoch) # reducing data rank
power_e, plv_e = _single_epoch_tfr(
data=epoch, is_free_ori=is_free_ori, K=K, Ws=Ws, use_fft=use_fft,
decim=decim, shape=shape, with_plv=with_plv, with_power=with_power)
power += power_e
if with_plv:
plv += plv_e
return power, plv
def _single_epoch_tfr(data, is_free_ori, K, Ws, use_fft, decim, shape,
with_plv, with_power):
"""Compute single trial TFRs, either ITC, power or raw TFR."""
tfr_e = np.zeros(shape, dtype=np.float) # power or raw TFR
# phase lock
plv_e = np.zeros(shape, dtype=np.complex) if with_plv else None
n_sources, _, n_times = shape
for f, w in enumerate(Ws):
tfr_ = cwt(data, [w], use_fft=use_fft, decim=decim)
tfr_ = np.asfortranarray(tfr_.reshape(len(data), -1))
# phase lock and power at freq f
if with_plv:
plv_f = np.zeros((n_sources, n_times), dtype=np.complex)
tfr_f = np.zeros((n_sources, n_times), dtype=np.float)
for k, t in enumerate([np.real(tfr_), np.imag(tfr_)]):
sol = np.dot(K, t)
sol_pick_normal = sol
if is_free_ori:
sol_pick_normal = sol[2::3]
if with_plv:
if k == 0: # real
plv_f += sol_pick_normal
else: # imag
plv_f += 1j * sol_pick_normal
if is_free_ori:
logger.debug('combining the current components...')
sol = combine_xyz(sol, square=with_power)
elif with_power:
sol *= sol
tfr_f += sol
del sol
tfr_e[:, f, :] += tfr_f
del tfr_f
if with_plv:
plv_f /= np.abs(plv_f)
plv_e[:, f, :] += plv_f
del plv_f
return tfr_e, plv_e
@verbose
def _source_induced_power(epochs, inverse_operator, freqs, label=None,
lambda2=1.0 / 9.0, method="dSPM", nave=1, n_cycles=5,
decim=1, use_fft=False, pca=True, pick_ori="normal",
n_jobs=1, with_plv=True, zero_mean=False,
prepared=False, method_params=None, verbose=None):
"""Aux function for source induced power."""
epochs_data = epochs.get_data()
K, sel, Vh, vertno, is_free_ori, noise_norm = _prepare_source_params(
inst=epochs, inverse_operator=inverse_operator, label=label,
lambda2=lambda2, method=method, nave=nave, pca=pca, pick_ori=pick_ori,
prepared=prepared, method_params=method_params, verbose=verbose)
inv = inverse_operator
parallel, my_compute_source_tfrs, n_jobs = parallel_func(
_compute_pow_plv, n_jobs)
Fs = epochs.info['sfreq'] # sampling in Hz
logger.info('Computing source power ...')
Ws = morlet(Fs, freqs, n_cycles=n_cycles, zero_mean=zero_mean)
n_jobs = min(n_jobs, len(epochs_data))
out = parallel(my_compute_source_tfrs(data=data, K=K, sel=sel, Ws=Ws,
source_ori=inv['source_ori'],
use_fft=use_fft, Vh=Vh,
with_plv=with_plv, with_power=True,
pick_ori=pick_ori, decim=decim)
for data in np.array_split(epochs_data, n_jobs))
power = sum(o[0] for o in out)
power /= len(epochs_data) # average power over epochs
if with_plv:
plv = sum(o[1] for o in out)
plv = np.abs(plv)
plv /= len(epochs_data) # average power over epochs
else:
plv = None
if method != "MNE":
power *= noise_norm.ravel()[:, None, None] ** 2
return power, plv, vertno
@verbose
def source_induced_power(epochs, inverse_operator, freqs, label=None,
lambda2=1.0 / 9.0, method="dSPM", nave=1, n_cycles=5,
decim=1, use_fft=False, pick_ori=None,
baseline=None, baseline_mode='logratio', pca=True,
n_jobs=1, zero_mean=False, prepared=False,
method_params=None, verbose=None):
"""Compute induced power and phase lock.
Computation can optionally be restricted in a label.
Parameters
----------
epochs : instance of Epochs
The epochs.
inverse_operator : instance of InverseOperator
The inverse operator.
freqs : array
Array of frequencies of interest.
label : Label
Restricts the source estimates to a given label.
lambda2 : float
The regularization parameter of the minimum norm.
method : "MNE" | "dSPM" | "sLORETA" | "eLORETA"
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
nave : int
The number of averages used to scale the noise covariance matrix.
n_cycles : float | array of float
Number of cycles. Fixed number or one per frequency.
decim : int
Temporal decimation factor.
use_fft : bool
Do convolutions in time or frequency domain with FFT.
pick_ori : None | "normal"
If "normal", rather than pooling the orientations by taking the norm,
only the radial component is kept. This is only implemented
when working with loose orientations.
baseline : None (default) or tuple of length 2
The time interval to apply baseline correction.
If None do not apply it. If baseline is (a, b)
the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used
and if b is None then b is set to the end of the interval.
If baseline is equal to (None, None) all the time
interval is used.
baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'
Perform baseline correction by
- subtracting the mean of baseline values ('mean')
- dividing by the mean of baseline values ('ratio')
- dividing by the mean of baseline values and taking the log
('logratio')
- subtracting the mean of baseline values followed by dividing by
the mean of baseline values ('percent')
- subtracting the mean of baseline values and dividing by the
standard deviation of baseline values ('zscore')
- dividing by the mean of baseline values, taking the log, and
dividing by the standard deviation of log baseline values
('zlogratio')
pca : bool
If True, the true dimension of data is estimated before running
the time-frequency transforms. It reduces the computation times
e.g. with a dataset that was maxfiltered (true dim is 64).
%(n_jobs)s
zero_mean : bool
Make sure the wavelets are zero mean.
prepared : bool
If True, do not call :func:`prepare_inverse_operator`.
method_params : dict | None
Additional options for eLORETA. See Notes of :func:`apply_inverse`.
%(verbose)s
""" # noqa: E501
_check_option('method', method, INVERSE_METHODS)
_check_ori(pick_ori, inverse_operator['source_ori'],
inverse_operator['src'])
power, plv, vertno = _source_induced_power(
epochs, inverse_operator, freqs, label=label, lambda2=lambda2,
method=method, nave=nave, n_cycles=n_cycles, decim=decim,
use_fft=use_fft, pick_ori=pick_ori, pca=pca, n_jobs=n_jobs,
method_params=method_params, zero_mean=zero_mean,
prepared=prepared)
# Run baseline correction
power = rescale(power, epochs.times[::decim], baseline, baseline_mode,
copy=False)
return power, plv
@verbose
def compute_source_psd(raw, inverse_operator, lambda2=1. / 9., method="dSPM",
tmin=0., tmax=None, fmin=0., fmax=200.,
n_fft=2048, overlap=0.5, pick_ori=None, label=None,
nave=1, pca=True, prepared=False, method_params=None,
inv_split=None, bandwidth='hann', adaptive=False,
low_bias=False, n_jobs=1, return_sensor=False, dB=False,
verbose=None):
"""Compute source power spectral density (PSD).
Parameters
----------
raw : instance of Raw
The raw data
inverse_operator : instance of InverseOperator
The inverse operator
lambda2: float
The regularization parameter
method: "MNE" | "dSPM" | "sLORETA"
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
tmin : float
The beginning of the time interval of interest (in seconds).
Use 0. for the beginning of the file.
tmax : float | None
The end of the time interval of interest (in seconds). If None
stop at the end of the file.
fmin : float
The lower frequency of interest
fmax : float
The upper frequency of interest
n_fft: int
Window size for the FFT. Should be a power of 2.
overlap: float
The overlap fraction between windows. Should be between 0 and 1.
0 means no overlap.
pick_ori : None | "normal"
If "normal", rather than pooling the orientations by taking the norm,
only the radial component is kept. This is only implemented
when working with loose orientations.
label: Label
Restricts the source estimates to a given label
nave : int
The number of averages used to scale the noise covariance matrix.
pca: bool
If True, the true dimension of data is estimated before running
the time-frequency transforms. It reduces the computation times
e.g. with a dataset that was maxfiltered (true dim is 64).
prepared : bool
If True, do not call :func:`prepare_inverse_operator`.
method_params : dict | None
Additional options for eLORETA. See Notes of :func:`apply_inverse`.
.. versionadded:: 0.16
inv_split : int or None
Split inverse operator into inv_split parts in order to save memory.
.. versionadded:: 0.17
bandwidth : float | str
The bandwidth of the multi taper windowing function in Hz.
Can also be a string (e.g., 'hann') to use a single window.
For backward compatibility, the default is 'hann'.
.. versionadded:: 0.17
adaptive : bool
Use adaptive weights to combine the tapered spectra into PSD
(slow, use n_jobs >> 1 to speed up computation).
.. versionadded:: 0.17
low_bias : bool
Only use tapers with more than 90%% spectral concentration within
bandwidth.
.. versionadded:: 0.17
%(n_jobs)s
It is only used if adaptive=True.
.. versionadded:: 0.17
return_sensor : bool
If True, return the sensor PSDs as an EvokedArray.
.. versionadded:: 0.17
dB : bool
If True (default False), return output it decibels.
.. versionadded:: 0.17
%(verbose)s
Returns
-------
stc_psd : instance of SourceEstimate | VolSourceEstimate
The PSD of each of the sources.
sensor_psd : instance of EvokedArray
The PSD of each sensor. Only returned if `return_sensor` is True.
See Also
--------
compute_source_psd_epochs
Notes
-----
Each window is multiplied by a window before processing, so
using a non-zero overlap is recommended.
This function is different from :func:`compute_source_psd_epochs` in that:
1. ``bandwidth='hann'`` by default, skipping multitaper estimation
2. For convenience it wraps
:func:`mne.make_fixed_length_events` and :class:`mne.Epochs`.
Otherwise the two should produce identical results.
"""
tmin = 0. if tmin is None else float(tmin)
overlap = float(overlap)
if not 0 <= overlap < 1:
raise ValueError('Overlap must be at least 0 and less than 1, got %s'
% (overlap,))
n_fft = int(n_fft)
duration = ((1. - overlap) * n_fft) / raw.info['sfreq']
events = make_fixed_length_events(raw, 1, tmin, tmax, duration)
epochs = Epochs(raw, events, 1, 0, (n_fft - 1) / raw.info['sfreq'],
baseline=None)
out = compute_source_psd_epochs(
epochs, inverse_operator, lambda2, method, fmin, fmax,
pick_ori, label, nave, pca, inv_split, bandwidth, adaptive, low_bias,
True, n_jobs, prepared, method_params, return_sensor=True)
source_data = 0.
sensor_data = 0.
count = 0
for stc, evoked in out:
source_data += stc.data
sensor_data += evoked.data
count += 1
assert count > 0 # should be guaranteed by make_fixed_length_events
sensor_data /= count
source_data /= count
if dB:
np.log10(sensor_data, out=sensor_data)
sensor_data *= 10.
np.log10(source_data, out=source_data)
source_data *= 10.
evoked.data = sensor_data
evoked.nave = count
stc.data = source_data
out = stc
if return_sensor:
out = (out, evoked)
return out
def _compute_source_psd_epochs(epochs, inverse_operator, lambda2=1. / 9.,
method="dSPM", fmin=0., fmax=200.,
pick_ori=None, label=None, nave=1,
pca=True, inv_split=None, bandwidth=4.,
adaptive=False, low_bias=True, n_jobs=1,
prepared=False, method_params=None,
return_sensor=False):
"""Generate compute_source_psd_epochs."""
logger.info('Considering frequencies %g ... %g Hz' % (fmin, fmax))
K, sel, Vh, vertno, is_free_ori, noise_norm = _prepare_source_params(
inst=epochs, inverse_operator=inverse_operator, label=label,
lambda2=lambda2, method=method, nave=nave, pca=pca, pick_ori=pick_ori,
prepared=prepared, method_params=method_params, verbose=verbose)
# Simplify code with a tiny (rel. to other computations) penalty for eye
# mult
Vh = np.eye(K.shape[0]) if Vh is None else Vh
# split the inverse operator
if inv_split is not None:
K_split = np.array_split(K, inv_split)
else:
K_split = [K]
# compute DPSS windows
n_times = len(epochs.times)
sfreq = epochs.info['sfreq']
dpss, eigvals, adaptive = _compute_mt_params(
n_times, sfreq, bandwidth, low_bias, adaptive, verbose=False)
n_tapers = len(dpss)
try:
n_epochs = len(epochs)
except RuntimeError:
n_epochs = len(epochs.events)
extra = 'on at most %d epochs' % (n_epochs,)
else:
extra = 'on %d epochs' % (n_epochs,)
if isinstance(bandwidth, str):
bandwidth = '%s windowing' % (bandwidth,)
else:
bandwidth = '%d tapers with bandwidth %0.1f Hz' % (n_tapers, bandwidth)
logger.info('Using %s %s' % (bandwidth, extra))
if adaptive:
parallel, my_psd_from_mt_adaptive, n_jobs = \
parallel_func(_psd_from_mt_adaptive, n_jobs)
else:
weights = np.sqrt(eigvals)[np.newaxis, :, np.newaxis]
subject = _subject_from_inverse(inverse_operator)
iter_epochs = ProgressBar(n_epochs)
iter_epochs.iterable = epochs
evoked_info = pick_info(epochs.info, sel, verbose=False)
for k, e in enumerate(iter_epochs):
data = np.dot(Vh, e[sel]) # reducing data rank
# compute tapered spectra in sensor space
x_mt, freqs = _mt_spectra(data, dpss, sfreq)
if k == 0:
freq_mask = (freqs >= fmin) & (freqs <= fmax)
fstep = np.mean(np.diff(freqs))
evoked_info['sfreq'] = 1. / fstep
freqs = freqs[freq_mask]
# sensor space PSD
x_mt_sensor = np.empty((len(sel), x_mt.shape[1],
x_mt.shape[2]), dtype=x_mt.dtype)
for i in range(n_tapers):
x_mt_sensor[:, i, :] = np.dot(Vh.T, x_mt[:, i, :])
if adaptive:
out = parallel(my_psd_from_mt_adaptive(x, eigvals, freq_mask)
for x in np.array_split(x_mt_sensor,
min(n_jobs,
len(x_mt_sensor))))
sensor_psd = np.concatenate(out)
else:
x_mt_sensor = x_mt_sensor[:, :, freq_mask]
sensor_psd = _psd_from_mt(x_mt_sensor, weights)
# allocate space for output
psd = np.empty((K.shape[0], np.sum(freq_mask)))
# Optionally, we split the inverse operator into parts to save memory.
# Without splitting the tapered spectra in source space have size
# (n_vertices x n_tapers x n_times / 2)
pos = 0
for K_part in K_split:
# allocate space for tapered spectra in source space
x_mt_src = np.empty((K_part.shape[0], x_mt.shape[1],
x_mt.shape[2]), dtype=x_mt.dtype)
# apply inverse to each taper (faster than equiv einsum)
for i in range(n_tapers):
x_mt_src[:, i, :] = np.dot(K_part, x_mt[:, i, :])
# compute the psd
if adaptive:
out = parallel(my_psd_from_mt_adaptive(x, eigvals, freq_mask)
for x in np.array_split(x_mt_src,
min(n_jobs,
len(x_mt_src))))
this_psd = np.concatenate(out)
else:
x_mt_src = x_mt_src[:, :, freq_mask]
this_psd = _psd_from_mt(x_mt_src, weights)
psd[pos:pos + K_part.shape[0], :] = this_psd
pos += K_part.shape[0]
# combine orientations
if is_free_ori and pick_ori is None:
psd = combine_xyz(psd, square=False)
if method != "MNE":
psd *= noise_norm ** 2
out = _make_stc(psd, tmin=freqs[0], tstep=fstep, vertices=vertno,
subject=subject, src_type=inverse_operator['src'].kind)
if return_sensor:
comment = 'Epoch %d PSD' % (k,)
out = (out, EvokedArray(sensor_psd, evoked_info.copy(), freqs[0],
comment, nave))
# we return a generator object for "stream processing"
yield out
iter_epochs.update(n_epochs) # in case some were skipped
iter_epochs.__exit__(None, None, None)
@verbose
def compute_source_psd_epochs(epochs, inverse_operator, lambda2=1. / 9.,
method="dSPM", fmin=0., fmax=200.,
pick_ori=None, label=None, nave=1,
pca=True, inv_split=None, bandwidth=4.,
adaptive=False, low_bias=True,
return_generator=False, n_jobs=1,
prepared=False, method_params=None,
return_sensor=False, verbose=None):
"""Compute source power spectral density (PSD) from Epochs.
This uses the multi-taper method to compute the PSD for each epoch.
Parameters
----------
epochs : instance of Epochs
The raw data.
inverse_operator : instance of InverseOperator
The inverse operator.
lambda2 : float
The regularization parameter.
method : "MNE" | "dSPM" | "sLORETA" | "eLORETA"
Use minimum norm, dSPM (default), sLORETA, or eLORETA.
fmin : float
The lower frequency of interest.
fmax : float
The upper frequency of interest.
pick_ori : None | "normal"
If "normal", rather than pooling the orientations by taking the norm,
only the radial component is kept. This is only implemented
when working with loose orientations.
label : Label
Restricts the source estimates to a given label.
nave : int
The number of averages used to scale the noise covariance matrix.
pca : bool
If True, the true dimension of data is estimated before running
the time-frequency transforms. It reduces the computation times
e.g. with a dataset that was maxfiltered (true dim is 64).
inv_split : int or None
Split inverse operator into inv_split parts in order to save memory.
bandwidth : float | str
The bandwidth of the multi taper windowing function in Hz.
Can also be a string (e.g., 'hann') to use a single window.
adaptive : bool
Use adaptive weights to combine the tapered spectra into PSD
(slow, use n_jobs >> 1 to speed up computation).
low_bias : bool
Only use tapers with more than 90%% spectral concentration within
bandwidth.
return_generator : bool
Return a generator object instead of a list. This allows iterating
over the stcs without having to keep them all in memory.
%(n_jobs)s
It is only used if adaptive=True.
prepared : bool
If True, do not call :func:`prepare_inverse_operator`.
method_params : dict | None
Additional options for eLORETA. See Notes of :func:`apply_inverse`.
.. versionadded:: 0.16
return_sensor : bool
If True, also return the sensor PSD for each epoch as an EvokedArray.
.. versionadded:: 0.17
%(verbose)s
Returns
-------
out : list (or generator object)
A list (or generator) for the source space PSD (and optionally the
sensor PSD) for each epoch.
See Also
--------
compute_source_psd
"""
# use an auxiliary function so we can either return a generator or a list
stcs_gen = _compute_source_psd_epochs(
epochs, inverse_operator, lambda2=lambda2, method=method,
fmin=fmin, fmax=fmax, pick_ori=pick_ori, label=label,
nave=nave, pca=pca, inv_split=inv_split, bandwidth=bandwidth,
adaptive=adaptive, low_bias=low_bias, n_jobs=n_jobs, prepared=prepared,
method_params=method_params, return_sensor=return_sensor)
if return_generator:
# return generator object
return stcs_gen
else:
# return a list
stcs = list()
for stc in stcs_gen:
stcs.append(stc)
return stcs