Note
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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-Rémi 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)
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()
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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,
)
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Using control points [ 3.50724321 4.37721471 15.59502306]
Total running time of the script: (0 minutes 17.564 seconds)