-
Notifications
You must be signed in to change notification settings - Fork 1.3k
/
_lcmv.py
425 lines (351 loc) · 14.7 KB
/
_lcmv.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
"""Compute Linearly constrained minimum variance (LCMV) beamformer."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Roman Goj <roman.goj@gmail.com>
# Britta Westner <britta.wstnr@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
from ..rank import compute_rank
from ..io.meas_info import _simplify_info
from ..io.pick import pick_channels_cov, pick_info
from ..forward import _subject_from_forward
from ..minimum_norm.inverse import combine_xyz, _check_reference, _check_depth
from ..source_estimate import _make_stc, _get_src_type
from ..utils import (logger, verbose, _check_channels_spatial_filter,
_check_one_ch_type, _check_info_inv)
from ._compute_beamformer import (
_prepare_beamformer_input, _compute_power,
_compute_beamformer, _check_src_type, Beamformer, _proj_whiten_data)
@verbose
def make_lcmv(info, forward, data_cov, reg=0.05, noise_cov=None, label=None,
pick_ori=None, rank='info',
weight_norm='unit-noise-gain-invariant',
reduce_rank=False, depth=None, inversion='matrix', verbose=None):
"""Compute LCMV spatial filter.
Parameters
----------
%(info_not_none)s
Specifies the channels to include. Bad channels (in ``info['bads']``)
are not used.
forward : instance of Forward
Forward operator.
data_cov : instance of Covariance
The data covariance.
reg : float
The regularization for the whitened data covariance.
noise_cov : instance of Covariance
The noise covariance. If provided, whitening will be done. Providing a
noise covariance is mandatory if you mix sensor types, e.g.
gradiometers with magnetometers or EEG with MEG.
label : instance of Label
Restricts the LCMV solution to a given label.
%(pick_ori_bf)s
- ``'vector'``
Keeps the currents for each direction separate
%(rank_info)s
%(weight_norm)s
Defaults to ``'unit-noise-gain-invariant'``.
%(reduce_rank)s
%(depth)s
.. versionadded:: 0.18
%(inversion_bf)s
.. versionadded:: 0.21
%(verbose)s
Returns
-------
filters : instance of Beamformer
Dictionary containing filter weights from LCMV beamformer.
Contains the following keys:
'kind' : str
The type of beamformer, in this case 'LCMV'.
'weights' : array
The filter weights of the beamformer.
'data_cov' : instance of Covariance
The data covariance matrix used to compute the beamformer.
'noise_cov' : instance of Covariance | None
The noise covariance matrix used to compute the beamformer.
'whitener' : None | ndarray, shape (n_channels, n_channels)
Whitening matrix, provided if whitening was applied to the
covariance matrix and leadfield during computation of the
beamformer weights.
'weight_norm' : str | None
Type of weight normalization used to compute the filter
weights.
'pick-ori' : None | 'max-power' | 'normal' | 'vector'
The orientation in which the beamformer filters were computed.
'ch_names' : list of str
Channels used to compute the beamformer.
'proj' : array
Projections used to compute the beamformer.
'is_ssp' : bool
If True, projections were applied prior to filter computation.
'vertices' : list
Vertices for which the filter weights were computed.
'is_free_ori' : bool
If True, the filter was computed with free source orientation.
'n_sources' : int
Number of source location for which the filter weight were
computed.
'src_type' : str
Type of source space.
'source_nn' : ndarray, shape (n_sources, 3)
For each source location, the surface normal.
'proj' : ndarray, shape (n_channels, n_channels)
Projections used to compute the beamformer.
'subject' : str
The subject ID.
'rank' : int
The rank of the data covariance matrix used to compute the
beamformer weights.
'max-power-ori' : ndarray, shape (n_sources, 3) | None
When pick_ori='max-power', this fields contains the estimated
direction of maximum power at each source location.
'inversion' : 'single' | 'matrix'
Whether the spatial filters were computed for each dipole
separately or jointly for all dipoles at each vertex using a
matrix inversion.
Notes
-----
The original reference is :footcite:`VanVeenEtAl1997`.
To obtain the Sekihara unit-noise-gain vector beamformer, you should use
``weight_norm='unit-noise-gain', pick_ori='vector'`` followed by
:meth:`vec_stc.project('pca', src) <mne.VectorSourceEstimate.project>`.
.. versionchanged:: 0.21
The computations were extensively reworked, and the default for
``weight_norm`` was set to ``'unit-noise-gain-invariant'``.
References
----------
.. footbibliography::
"""
# check number of sensor types present in the data and ensure a noise cov
info = _simplify_info(info)
noise_cov, _, allow_mismatch = _check_one_ch_type(
'lcmv', info, forward, data_cov, noise_cov)
# XXX we need this extra picking step (can't just rely on minimum norm's
# because there can be a mismatch. Should probably add an extra arg to
# _prepare_beamformer_input at some point (later)
picks = _check_info_inv(info, forward, data_cov, noise_cov)
info = pick_info(info, picks)
data_rank = compute_rank(data_cov, rank=rank, info=info)
noise_rank = compute_rank(noise_cov, rank=rank, info=info)
for key in data_rank:
if (key not in noise_rank or data_rank[key] != noise_rank[key]) and \
not allow_mismatch:
raise ValueError('%s data rank (%s) did not match the noise '
'rank (%s)'
% (key, data_rank[key],
noise_rank.get(key, None)))
del noise_rank
rank = data_rank
logger.info('Making LCMV beamformer with rank %s' % (rank,))
del data_rank
depth = _check_depth(depth, 'depth_sparse')
if inversion == 'single':
depth['combine_xyz'] = False
is_free_ori, info, proj, vertno, G, whitener, nn, orient_std = \
_prepare_beamformer_input(
info, forward, label, pick_ori, noise_cov=noise_cov, rank=rank,
pca=False, **depth)
ch_names = list(info['ch_names'])
data_cov = pick_channels_cov(data_cov, include=ch_names)
Cm = data_cov._get_square()
if 'estimator' in data_cov:
del data_cov['estimator']
rank_int = sum(rank.values())
del rank
# compute spatial filter
n_orient = 3 if is_free_ori else 1
W, max_power_ori = _compute_beamformer(
G, Cm, reg, n_orient, weight_norm, pick_ori, reduce_rank, rank_int,
inversion=inversion, nn=nn, orient_std=orient_std,
whitener=whitener)
# get src type to store with filters for _make_stc
src_type = _get_src_type(forward['src'], vertno)
# get subject to store with filters
subject_from = _subject_from_forward(forward)
# Is the computed beamformer a scalar or vector beamformer?
is_free_ori = is_free_ori if pick_ori in [None, 'vector'] else False
is_ssp = bool(info['projs'])
filters = Beamformer(
kind='LCMV', weights=W, data_cov=data_cov, noise_cov=noise_cov,
whitener=whitener, weight_norm=weight_norm, pick_ori=pick_ori,
ch_names=ch_names, proj=proj, is_ssp=is_ssp, vertices=vertno,
is_free_ori=is_free_ori, n_sources=forward['nsource'],
src_type=src_type, source_nn=forward['source_nn'].copy(),
subject=subject_from, rank=rank_int, max_power_ori=max_power_ori,
inversion=inversion)
return filters
def _apply_lcmv(data, filters, info, tmin):
"""Apply LCMV spatial filter to data for source reconstruction."""
if isinstance(data, np.ndarray) and data.ndim == 2:
data = [data]
return_single = True
else:
return_single = False
W = filters['weights']
for i, M in enumerate(data):
if len(M) != len(filters['ch_names']):
raise ValueError('data and picks must have the same length')
if not return_single:
logger.info("Processing epoch : %d" % (i + 1))
M = _proj_whiten_data(M, info['projs'], filters)
# project to source space using beamformer weights
vector = False
if filters['is_free_ori']:
sol = np.dot(W, M)
if filters['pick_ori'] == 'vector':
vector = True
else:
logger.info('combining the current components...')
sol = combine_xyz(sol)
else:
# Linear inverse: do computation here or delayed
if (M.shape[0] < W.shape[0] and
filters['pick_ori'] != 'max-power'):
sol = (W, M)
else:
sol = np.dot(W, M)
tstep = 1.0 / info['sfreq']
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
yield _make_stc(sol, vertices=filters['vertices'], tmin=tmin,
tstep=tstep, subject=filters['subject'],
vector=vector, source_nn=filters['source_nn'],
src_type=filters['src_type'], warn_text=warn_text)
logger.info('[done]')
@verbose
def apply_lcmv(evoked, filters, *, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on evoked data.
Parameters
----------
evoked : Evoked
Evoked data to invert.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate | VectorSourceEstimate
Source time courses.
See Also
--------
make_lcmv, apply_lcmv_raw, apply_lcmv_epochs, apply_lcmv_cov
Notes
-----
.. versionadded:: 0.18
"""
_check_reference(evoked)
info = evoked.info
data = evoked.data
tmin = evoked.times[0]
sel = _check_channels_spatial_filter(evoked.ch_names, filters)
data = data[sel]
stc = _apply_lcmv(data=data, filters=filters, info=info,
tmin=tmin)
return next(stc)
@verbose
def apply_lcmv_epochs(epochs, filters, *, return_generator=False,
verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on single trial data.
Parameters
----------
epochs : Epochs
Single trial epochs.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights)
Filter weights returned from :func:`make_lcmv`.
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.
%(verbose)s
Returns
-------
stc: list | generator of (SourceEstimate | VolSourceEstimate)
The source estimates for all epochs.
See Also
--------
make_lcmv, apply_lcmv_raw, apply_lcmv, apply_lcmv_cov
"""
_check_reference(epochs)
info = epochs.info
tmin = epochs.times[0]
sel = _check_channels_spatial_filter(epochs.ch_names, filters)
data = epochs.get_data()[:, sel, :]
stcs = _apply_lcmv(data=data, filters=filters, info=info,
tmin=tmin)
if not return_generator:
stcs = [s for s in stcs]
return stcs
@verbose
def apply_lcmv_raw(raw, filters, start=None, stop=None, *, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
on raw data.
Parameters
----------
raw : mne.io.Raw
Raw data to invert.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
start : int
Index of first time sample (index not time is seconds).
stop : int
Index of first time sample not to include (index not time is seconds).
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate
Source time courses.
See Also
--------
make_lcmv, apply_lcmv_epochs, apply_lcmv, apply_lcmv_cov
"""
_check_reference(raw)
info = raw.info
sel = _check_channels_spatial_filter(raw.ch_names, filters)
data, times = raw[sel, start:stop]
tmin = times[0]
stc = _apply_lcmv(data=data, filters=filters, info=info, tmin=tmin)
return next(stc)
@verbose
def apply_lcmv_cov(data_cov, filters, verbose=None):
"""Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights.
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights
to a data covariance matrix to estimate source power.
Parameters
----------
data_cov : instance of Covariance
Data covariance matrix.
filters : instance of Beamformer
LCMV spatial filter (beamformer weights).
Filter weights returned from :func:`make_lcmv`.
%(verbose)s
Returns
-------
stc : SourceEstimate | VolSourceEstimate
Source power.
See Also
--------
make_lcmv, apply_lcmv, apply_lcmv_epochs, apply_lcmv_raw
"""
sel = _check_channels_spatial_filter(data_cov.ch_names, filters)
sel_names = [data_cov.ch_names[ii] for ii in sel]
data_cov = pick_channels_cov(data_cov, sel_names)
n_orient = filters['weights'].shape[0] // filters['n_sources']
# Need to project and whiten along both dimensions
data = _proj_whiten_data(data_cov['data'].T, data_cov['projs'], filters)
data = _proj_whiten_data(data.T, data_cov['projs'], filters)
del data_cov
source_power = _compute_power(data, filters['weights'], n_orient)
# compatibility with 0.16, add src_type as None if not present:
filters, warn_text = _check_src_type(filters)
return _make_stc(source_power, vertices=filters['vertices'],
src_type=filters['src_type'], tmin=0., tstep=1.,
subject=filters['subject'],
source_nn=filters['source_nn'], warn_text=warn_text)