Note
Go to the end to download the full example code.
Decoding source space data#
Decoding to MEG data in source space on the left cortical surface. Here univariate feature selection is employed for speed purposes to confine the classification to a small number of potentially relevant features. The classifier then is trained to selected features of epochs in source space.
# Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import matplotlib.pyplot as plt
import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import mne
from mne.decoding import LinearModel, SlidingEstimator, cross_val_multiscore, get_coef
from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
print(__doc__)
data_path = mne.datasets.sample.data_path()
meg_path = data_path / "MEG" / "sample"
fname_fwd = meg_path / "sample_audvis-meg-oct-6-fwd.fif"
fname_evoked = meg_path / "sample_audvis-ave.fif"
subjects_dir = data_path / "subjects"
Set parameters
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
event_fname = meg_path / "sample_audvis_filt-0-40_raw-eve.fif"
fname_cov = meg_path / "sample_audvis-cov.fif"
fname_inv = meg_path / "sample_audvis-meg-oct-6-meg-inv.fif"
tmin, tmax = -0.2, 0.8
event_id = dict(aud_r=2, vis_r=4) # load contra-lateral conditions
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(None, 10.0, fir_design="firwin")
events = mne.read_events(event_fname)
# Set up pick list: MEG - bad channels (modify to your needs)
raw.info["bads"] += ["MEG 2443"] # mark bads
picks = mne.pick_types(
raw.info, meg=True, eeg=False, stim=True, eog=True, exclude="bads"
)
# Read epochs
epochs = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=(None, 0),
preload=True,
reject=dict(grad=4000e-13, eog=150e-6),
decim=5,
) # decimate to save memory and increase speed
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up low-pass filter at 10 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal lowpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Upper passband edge: 10.00 Hz
- Upper transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 11.25 Hz)
- Filter length: 199 samples (1.325 s)
[Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.6s
Not setting metadata
143 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Using data from preloaded Raw for 143 events and 151 original time points (prior to decimation) ...
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
28 bad epochs dropped
Compute inverse solution
snr = 3.0
noise_cov = mne.read_cov(fname_cov)
inverse_operator = read_inverse_operator(fname_inv)
stcs = apply_inverse_epochs(
epochs,
inverse_operator,
lambda2=1.0 / snr**2,
verbose=False,
method="dSPM",
pick_ori="normal",
)
366 x 366 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
Reading inverse operator info...
[done]
Reading inverse operator decomposition...
[done]
305 x 305 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Noise covariance matrix read.
22494 x 22494 diagonal covariance (kind = 2) found.
Source covariance matrix read.
22494 x 22494 diagonal covariance (kind = 6) found.
Orientation priors read.
22494 x 22494 diagonal covariance (kind = 5) found.
Depth priors read.
Did not find the desired covariance matrix (kind = 3)
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
2 source spaces read
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Source spaces transformed to the inverse solution coordinate frame
Decoding in sensor space using a logistic regression
# Retrieve source space data into an array
X = np.array([stc.lh_data for stc in stcs]) # only keep left hemisphere
y = epochs.events[:, 2]
# prepare a series of classifier applied at each time sample
clf = make_pipeline(
StandardScaler(), # z-score normalization
SelectKBest(f_classif, k=500), # select features for speed
LinearModel(LogisticRegression(C=1, solver="liblinear")),
)
time_decod = SlidingEstimator(clf, scoring="roc_auc")
# Run cross-validated decoding analyses:
scores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=None)
# Plot average decoding scores of 5 splits
fig, ax = plt.subplots(1)
ax.plot(epochs.times, scores.mean(0), label="score")
ax.axhline(0.5, color="k", linestyle="--", label="chance")
ax.axvline(0, color="k")
plt.legend()
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:03, 9.87it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 14.95it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 18.05it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 17.05it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 18.80it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 20.18it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 21.28it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:01, 22.19it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:01, 20.79it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:00, 21.58it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:00, 22.28it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:00, 22.89it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:00, 23.43it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 23.90it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 24.32it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 24.70it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 25.03it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 23.68it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:00<00:00, 24.06it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:00<00:00, 24.41it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:00<00:00, 24.74it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:00<00:00, 25.02it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:00<00:00, 23.83it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:01<00:00, 24.15it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:01<00:00, 24.46it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 24.75it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 23.69it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 24.00it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 24.29it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 24.56it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 24.79it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 24.03it/s]
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:02, 14.86it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 19.95it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 22.52it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 24.08it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 25.12it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 22.22it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 23.18it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:00, 23.95it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:00, 24.58it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:00, 25.11it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:00, 25.56it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:00, 25.95it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:00, 26.27it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 26.55it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 26.81it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 27.03it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 25.26it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 25.58it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:00<00:00, 25.86it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:00<00:00, 26.12it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:00<00:00, 26.34it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:00<00:00, 26.55it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:00<00:00, 25.12it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:00<00:00, 25.39it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:00<00:00, 25.65it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 25.88it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 26.10it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 24.87it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 25.12it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 25.37it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 26.01it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 25.71it/s]
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:02, 14.85it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 19.93it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 22.51it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 24.06it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 21.16it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 22.36it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 23.30it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:00, 24.06it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:00, 24.69it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:00, 25.20it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:00, 25.64it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:00, 26.02it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:00, 24.15it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 24.59it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 24.99it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 25.34it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 25.65it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 25.89it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:00<00:00, 24.43it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:00<00:00, 24.76it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:00<00:00, 25.07it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:00<00:00, 25.36it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:00<00:00, 25.62it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:00<00:00, 24.37it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:01<00:00, 24.67it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 24.96it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 25.22it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 25.47it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 25.70it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 24.56it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 25.28it/s]
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:02, 14.83it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 19.92it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 22.50it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 19.75it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 21.32it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 22.50it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 23.42it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:00, 24.16it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:00, 22.28it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:00, 22.99it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:00, 23.60it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:00, 24.12it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:00, 24.59it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 24.99it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 25.35it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 25.63it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 24.13it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 24.51it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:00<00:00, 24.85it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:00<00:00, 25.17it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:00<00:00, 23.91it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:00<00:00, 24.25it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:00<00:00, 24.57it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:00<00:00, 24.86it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:01<00:00, 25.14it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 24.01it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 24.31it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 24.60it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 24.87it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 23.85it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 24.51it/s]
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:02, 14.83it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 19.92it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 17.79it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 19.94it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 21.47it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 22.64it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 23.56it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:01, 21.68it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:00, 22.49it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:00, 23.18it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:00, 23.77it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:00, 24.29it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:00, 24.75it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 25.14it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 25.50it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 23.97it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 24.37it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 24.73it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:00<00:00, 25.05it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:00<00:00, 25.35it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:00<00:00, 24.07it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:00<00:00, 24.40it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:00<00:00, 24.72it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:00<00:00, 25.01it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:01<00:00, 25.28it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 24.13it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 24.43it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 24.71it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 24.98it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 25.23it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 25.24it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 24.70it/s]
To investigate weights, we need to retrieve the patterns of a fitted model
# The fitting needs not be cross validated because the weights are based on
# the training sets
time_decod.fit(X, y)
# Retrieve patterns after inversing the z-score normalization step:
patterns = get_coef(time_decod, "patterns_", inverse_transform=True)
stc = stcs[0] # for convenience, lookup parameters from first stc
vertices = [stc.lh_vertno, np.array([], int)] # empty array for right hemi
stc_feat = mne.SourceEstimate(
np.abs(patterns),
vertices=vertices,
tmin=stc.tmin,
tstep=stc.tstep,
subject="sample",
)
brain = stc_feat.plot(
views=["lat"],
transparent=True,
initial_time=0.1,
time_unit="s",
subjects_dir=subjects_dir,
)
0%| | Fitting SlidingEstimator : 0/31 [00:00<?, ?it/s]
3%|▎ | Fitting SlidingEstimator : 1/31 [00:00<00:03, 9.86it/s]
6%|▋ | Fitting SlidingEstimator : 2/31 [00:00<00:01, 14.83it/s]
10%|▉ | Fitting SlidingEstimator : 3/31 [00:00<00:01, 14.79it/s]
13%|█▎ | Fitting SlidingEstimator : 4/31 [00:00<00:01, 14.79it/s]
16%|█▌ | Fitting SlidingEstimator : 5/31 [00:00<00:01, 16.32it/s]
19%|█▉ | Fitting SlidingEstimator : 6/31 [00:00<00:01, 16.02it/s]
23%|██▎ | Fitting SlidingEstimator : 7/31 [00:00<00:01, 15.81it/s]
26%|██▌ | Fitting SlidingEstimator : 8/31 [00:00<00:01, 16.98it/s]
29%|██▉ | Fitting SlidingEstimator : 9/31 [00:00<00:01, 16.65it/s]
32%|███▏ | Fitting SlidingEstimator : 10/31 [00:00<00:01, 17.60it/s]
35%|███▌ | Fitting SlidingEstimator : 11/31 [00:00<00:01, 17.23it/s]
39%|███▊ | Fitting SlidingEstimator : 12/31 [00:00<00:01, 18.05it/s]
42%|████▏ | Fitting SlidingEstimator : 13/31 [00:00<00:01, 17.66it/s]
45%|████▌ | Fitting SlidingEstimator : 14/31 [00:00<00:00, 18.38it/s]
48%|████▊ | Fitting SlidingEstimator : 15/31 [00:00<00:00, 17.98it/s]
52%|█████▏ | Fitting SlidingEstimator : 16/31 [00:00<00:00, 18.63it/s]
55%|█████▍ | Fitting SlidingEstimator : 17/31 [00:00<00:00, 19.22it/s]
58%|█████▊ | Fitting SlidingEstimator : 18/31 [00:00<00:00, 18.75it/s]
61%|██████▏ | Fitting SlidingEstimator : 19/31 [00:01<00:00, 19.31it/s]
65%|██████▍ | Fitting SlidingEstimator : 20/31 [00:01<00:00, 18.86it/s]
68%|██████▊ | Fitting SlidingEstimator : 21/31 [00:01<00:00, 19.37it/s]
71%|███████ | Fitting SlidingEstimator : 22/31 [00:01<00:00, 18.94it/s]
74%|███████▍ | Fitting SlidingEstimator : 23/31 [00:01<00:00, 19.44it/s]
77%|███████▋ | Fitting SlidingEstimator : 24/31 [00:01<00:00, 19.01it/s]
81%|████████ | Fitting SlidingEstimator : 25/31 [00:01<00:00, 18.65it/s]
84%|████████▍ | Fitting SlidingEstimator : 26/31 [00:01<00:00, 19.12it/s]
87%|████████▋ | Fitting SlidingEstimator : 27/31 [00:01<00:00, 18.76it/s]
90%|█████████ | Fitting SlidingEstimator : 28/31 [00:01<00:00, 19.22it/s]
94%|█████████▎| Fitting SlidingEstimator : 29/31 [00:01<00:00, 18.85it/s]
97%|█████████▋| Fitting SlidingEstimator : 30/31 [00:01<00:00, 19.29it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 19.49it/s]
100%|██████████| Fitting SlidingEstimator : 31/31 [00:01<00:00, 18.89it/s]
Using control points [ 3.50724321 4.37721471 15.59502306]
Total running time of the script: (0 minutes 17.628 seconds)
Estimated memory usage: 364 MB