Note
Go to the end to download the full example code
Decoding sensor space data with generalization across time and conditions#
This example runs the analysis described in [1]. It illustrates how one can fit a linear classifier to identify a discriminatory topography at a given time instant and subsequently assess whether this linear model can accurately predict all of the time samples of a second set of conditions.
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
import mne
from mne.datasets import sample
from mne.decoding import GeneralizingEstimator
print(__doc__)
# Preprocess data
data_path = sample.data_path()
# Load and filter data, set up epochs
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
events_fname = meg_path / "sample_audvis_filt-0-40_raw-eve.fif"
raw = mne.io.read_raw_fif(raw_fname, preload=True)
picks = mne.pick_types(raw.info, meg=True, exclude="bads") # Pick MEG channels
raw.filter(1.0, 30.0, fir_design="firwin") # Band pass filtering signals
events = mne.read_events(events_fname)
event_id = {
"Auditory/Left": 1,
"Auditory/Right": 2,
"Visual/Left": 3,
"Visual/Right": 4,
}
tmin = -0.050
tmax = 0.400
# decimate to make the example faster to run, but then use verbose='error' in
# the Epochs constructor to suppress warning about decimation causing aliasing
decim = 2
epochs = mne.Epochs(
raw,
events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
reject=dict(mag=5e-12),
decim=decim,
verbose="error",
)
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 band-pass filter from 1 - 30 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 497 samples (3.310 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: 0.5s
We will train the classifier on all left visual vs auditory trials and test on all right visual vs auditory trials.
clf = make_pipeline(
StandardScaler(),
LogisticRegression(solver="liblinear"), # liblinear is faster than lbfgs
)
time_gen = GeneralizingEstimator(clf, scoring="roc_auc", n_jobs=None, verbose=True)
# Fit classifiers on the epochs where the stimulus was presented to the left.
# Note that the experimental condition y indicates auditory or visual
time_gen.fit(X=epochs["Left"].get_data(copy=False), y=epochs["Left"].events[:, 2] > 2)
0%| | Fitting GeneralizingEstimator : 0/35 [00:00<?, ?it/s]
3%|▎ | Fitting GeneralizingEstimator : 1/35 [00:00<00:01, 28.93it/s]
9%|▊ | Fitting GeneralizingEstimator : 3/35 [00:00<00:00, 44.19it/s]
14%|█▍ | Fitting GeneralizingEstimator : 5/35 [00:00<00:00, 49.35it/s]
20%|██ | Fitting GeneralizingEstimator : 7/35 [00:00<00:00, 51.92it/s]
26%|██▌ | Fitting GeneralizingEstimator : 9/35 [00:00<00:00, 53.50it/s]
34%|███▍ | Fitting GeneralizingEstimator : 12/35 [00:00<00:00, 60.11it/s]
43%|████▎ | Fitting GeneralizingEstimator : 15/35 [00:00<00:00, 64.84it/s]
51%|█████▏ | Fitting GeneralizingEstimator : 18/35 [00:00<00:00, 68.35it/s]
57%|█████▋ | Fitting GeneralizingEstimator : 20/35 [00:00<00:00, 67.09it/s]
66%|██████▌ | Fitting GeneralizingEstimator : 23/35 [00:00<00:00, 69.75it/s]
71%|███████▏ | Fitting GeneralizingEstimator : 25/35 [00:00<00:00, 68.46it/s]
74%|███████▍ | Fitting GeneralizingEstimator : 26/35 [00:00<00:00, 64.24it/s]
80%|████████ | Fitting GeneralizingEstimator : 28/35 [00:00<00:00, 63.72it/s]
86%|████████▌ | Fitting GeneralizingEstimator : 30/35 [00:00<00:00, 63.25it/s]
91%|█████████▏| Fitting GeneralizingEstimator : 32/35 [00:00<00:00, 62.84it/s]
100%|██████████| Fitting GeneralizingEstimator : 35/35 [00:00<00:00, 65.72it/s]
100%|██████████| Fitting GeneralizingEstimator : 35/35 [00:00<00:00, 64.80it/s]
Score on the epochs where the stimulus was presented to the right.
scores = time_gen.score(
X=epochs["Right"].get_data(copy=False), y=epochs["Right"].events[:, 2] > 2
)
0%| | Scoring GeneralizingEstimator : 0/1225 [00:00<?, ?it/s]
1%| | Scoring GeneralizingEstimator : 9/1225 [00:00<00:04, 263.23it/s]
1%|▏ | Scoring GeneralizingEstimator : 17/1225 [00:00<00:04, 248.19it/s]
2%|▏ | Scoring GeneralizingEstimator : 25/1225 [00:00<00:04, 243.55it/s]
3%|▎ | Scoring GeneralizingEstimator : 34/1225 [00:00<00:04, 249.23it/s]
3%|▎ | Scoring GeneralizingEstimator : 42/1225 [00:00<00:04, 246.38it/s]
4%|▍ | Scoring GeneralizingEstimator : 50/1225 [00:00<00:04, 244.59it/s]
5%|▍ | Scoring GeneralizingEstimator : 58/1225 [00:00<00:04, 243.14it/s]
6%|▌ | Scoring GeneralizingEstimator : 69/1225 [00:00<00:04, 255.06it/s]
6%|▋ | Scoring GeneralizingEstimator : 78/1225 [00:00<00:04, 256.36it/s]
7%|▋ | Scoring GeneralizingEstimator : 87/1225 [00:00<00:04, 257.43it/s]
8%|▊ | Scoring GeneralizingEstimator : 97/1225 [00:00<00:04, 261.68it/s]
9%|▊ | Scoring GeneralizingEstimator : 105/1225 [00:00<00:04, 258.79it/s]
9%|▉ | Scoring GeneralizingEstimator : 114/1225 [00:00<00:04, 259.37it/s]
10%|█ | Scoring GeneralizingEstimator : 125/1225 [00:00<00:04, 265.11it/s]
11%|█ | Scoring GeneralizingEstimator : 133/1225 [00:00<00:04, 262.13it/s]
12%|█▏ | Scoring GeneralizingEstimator : 142/1225 [00:00<00:04, 262.02it/s]
12%|█▏ | Scoring GeneralizingEstimator : 153/1225 [00:00<00:04, 266.85it/s]
13%|█▎ | Scoring GeneralizingEstimator : 161/1225 [00:00<00:04, 264.26it/s]
14%|█▍ | Scoring GeneralizingEstimator : 174/1225 [00:00<00:03, 273.42it/s]
15%|█▌ | Scoring GeneralizingEstimator : 185/1225 [00:00<00:03, 277.34it/s]
16%|█▌ | Scoring GeneralizingEstimator : 193/1225 [00:00<00:03, 274.18it/s]
17%|█▋ | Scoring GeneralizingEstimator : 207/1225 [00:00<00:03, 284.40it/s]
18%|█▊ | Scoring GeneralizingEstimator : 219/1225 [00:00<00:03, 289.29it/s]
19%|█▊ | Scoring GeneralizingEstimator : 228/1225 [00:00<00:03, 287.55it/s]
20%|█▉ | Scoring GeneralizingEstimator : 242/1225 [00:00<00:03, 296.19it/s]
21%|██ | Scoring GeneralizingEstimator : 255/1225 [00:00<00:03, 302.04it/s]
22%|██▏ | Scoring GeneralizingEstimator : 264/1225 [00:00<00:03, 299.61it/s]
22%|██▏ | Scoring GeneralizingEstimator : 275/1225 [00:00<00:03, 301.19it/s]
23%|██▎ | Scoring GeneralizingEstimator : 284/1225 [00:00<00:03, 298.93it/s]
24%|██▍ | Scoring GeneralizingEstimator : 293/1225 [00:01<00:03, 296.54it/s]
25%|██▍ | Scoring GeneralizingEstimator : 304/1225 [00:01<00:03, 298.25it/s]
26%|██▌ | Scoring GeneralizingEstimator : 316/1225 [00:01<00:03, 301.72it/s]
27%|██▋ | Scoring GeneralizingEstimator : 325/1225 [00:01<00:03, 299.31it/s]
27%|██▋ | Scoring GeneralizingEstimator : 335/1225 [00:01<00:02, 299.09it/s]
28%|██▊ | Scoring GeneralizingEstimator : 347/1225 [00:01<00:02, 302.31it/s]
29%|██▉ | Scoring GeneralizingEstimator : 357/1225 [00:01<00:02, 301.88it/s]
30%|███ | Scoring GeneralizingEstimator : 368/1225 [00:01<00:02, 302.77it/s]
31%|███ | Scoring GeneralizingEstimator : 377/1225 [00:01<00:02, 300.38it/s]
31%|███▏ | Scoring GeneralizingEstimator : 384/1225 [00:01<00:02, 295.00it/s]
32%|███▏ | Scoring GeneralizingEstimator : 390/1225 [00:01<00:02, 288.16it/s]
32%|███▏ | Scoring GeneralizingEstimator : 396/1225 [00:01<00:02, 281.42it/s]
33%|███▎ | Scoring GeneralizingEstimator : 406/1225 [00:01<00:02, 282.17it/s]
34%|███▍ | Scoring GeneralizingEstimator : 416/1225 [00:01<00:02, 282.91it/s]
35%|███▍ | Scoring GeneralizingEstimator : 425/1225 [00:01<00:02, 281.96it/s]
35%|███▌ | Scoring GeneralizingEstimator : 433/1225 [00:01<00:02, 279.41it/s]
36%|███▌ | Scoring GeneralizingEstimator : 442/1225 [00:01<00:02, 278.68it/s]
37%|███▋ | Scoring GeneralizingEstimator : 450/1225 [00:01<00:02, 276.22it/s]
37%|███▋ | Scoring GeneralizingEstimator : 459/1225 [00:01<00:02, 275.62it/s]
38%|███▊ | Scoring GeneralizingEstimator : 471/1225 [00:01<00:02, 279.85it/s]
39%|███▉ | Scoring GeneralizingEstimator : 480/1225 [00:01<00:02, 278.19it/s]
40%|███▉ | Scoring GeneralizingEstimator : 488/1225 [00:01<00:02, 275.92it/s]
41%|████ | Scoring GeneralizingEstimator : 497/1225 [00:01<00:02, 275.43it/s]
41%|████▏ | Scoring GeneralizingEstimator : 507/1225 [00:01<00:02, 275.42it/s]
42%|████▏ | Scoring GeneralizingEstimator : 517/1225 [00:01<00:02, 276.38it/s]
43%|████▎ | Scoring GeneralizingEstimator : 529/1225 [00:01<00:02, 280.46it/s]
44%|████▍ | Scoring GeneralizingEstimator : 538/1225 [00:01<00:02, 279.69it/s]
45%|████▍ | Scoring GeneralizingEstimator : 550/1225 [00:01<00:02, 283.62it/s]
46%|████▌ | Scoring GeneralizingEstimator : 564/1225 [00:01<00:02, 290.44it/s]
47%|████▋ | Scoring GeneralizingEstimator : 573/1225 [00:02<00:02, 288.92it/s]
48%|████▊ | Scoring GeneralizingEstimator : 586/1225 [00:02<00:02, 293.83it/s]
49%|████▉ | Scoring GeneralizingEstimator : 599/1225 [00:02<00:02, 297.22it/s]
50%|████▉ | Scoring GeneralizingEstimator : 609/1225 [00:02<00:02, 295.26it/s]
50%|█████ | Scoring GeneralizingEstimator : 618/1225 [00:02<00:02, 293.71it/s]
51%|█████▏ | Scoring GeneralizingEstimator : 628/1225 [00:02<00:02, 293.80it/s]
52%|█████▏ | Scoring GeneralizingEstimator : 637/1225 [00:02<00:02, 292.35it/s]
53%|█████▎ | Scoring GeneralizingEstimator : 648/1225 [00:02<00:01, 293.90it/s]
54%|█████▍ | Scoring GeneralizingEstimator : 659/1225 [00:02<00:01, 294.91it/s]
55%|█████▍ | Scoring GeneralizingEstimator : 669/1225 [00:02<00:01, 294.56it/s]
56%|█████▌ | Scoring GeneralizingEstimator : 681/1225 [00:02<00:01, 297.48it/s]
56%|█████▋ | Scoring GeneralizingEstimator : 691/1225 [00:02<00:01, 297.33it/s]
57%|█████▋ | Scoring GeneralizingEstimator : 702/1225 [00:02<00:01, 298.60it/s]
58%|█████▊ | Scoring GeneralizingEstimator : 712/1225 [00:02<00:01, 298.26it/s]
59%|█████▉ | Scoring GeneralizingEstimator : 720/1225 [00:02<00:01, 295.14it/s]
60%|█████▉ | Scoring GeneralizingEstimator : 729/1225 [00:02<00:01, 293.65it/s]
60%|██████ | Scoring GeneralizingEstimator : 739/1225 [00:02<00:01, 293.77it/s]
61%|██████ | Scoring GeneralizingEstimator : 747/1225 [00:02<00:01, 290.75it/s]
62%|██████▏ | Scoring GeneralizingEstimator : 757/1225 [00:02<00:01, 290.90it/s]
63%|██████▎ | Scoring GeneralizingEstimator : 766/1225 [00:02<00:01, 289.61it/s]
63%|██████▎ | Scoring GeneralizingEstimator : 775/1225 [00:02<00:01, 288.41it/s]
64%|██████▍ | Scoring GeneralizingEstimator : 784/1225 [00:02<00:01, 287.26it/s]
65%|██████▍ | Scoring GeneralizingEstimator : 795/1225 [00:02<00:01, 288.95it/s]
66%|██████▌ | Scoring GeneralizingEstimator : 805/1225 [00:02<00:01, 289.22it/s]
67%|██████▋ | Scoring GeneralizingEstimator : 818/1225 [00:02<00:01, 293.99it/s]
68%|██████▊ | Scoring GeneralizingEstimator : 833/1225 [00:02<00:01, 301.30it/s]
69%|██████▊ | Scoring GeneralizingEstimator : 842/1225 [00:02<00:01, 299.40it/s]
70%|███████ | Scoring GeneralizingEstimator : 858/1225 [00:02<00:01, 307.94it/s]
71%|███████▏ | Scoring GeneralizingEstimator : 875/1225 [00:02<00:01, 317.68it/s]
72%|███████▏ | Scoring GeneralizingEstimator : 884/1225 [00:03<00:01, 315.11it/s]
73%|███████▎ | Scoring GeneralizingEstimator : 896/1225 [00:03<00:01, 317.08it/s]
74%|███████▍ | Scoring GeneralizingEstimator : 907/1225 [00:03<00:01, 317.51it/s]
75%|███████▍ | Scoring GeneralizingEstimator : 916/1225 [00:03<00:00, 314.93it/s]
76%|███████▌ | Scoring GeneralizingEstimator : 929/1225 [00:03<00:00, 318.38it/s]
77%|███████▋ | Scoring GeneralizingEstimator : 940/1225 [00:03<00:00, 318.65it/s]
77%|███████▋ | Scoring GeneralizingEstimator : 948/1225 [00:03<00:00, 314.44it/s]
78%|███████▊ | Scoring GeneralizingEstimator : 961/1225 [00:03<00:00, 317.92it/s]
80%|███████▉ | Scoring GeneralizingEstimator : 974/1225 [00:03<00:00, 321.10it/s]
80%|████████ | Scoring GeneralizingEstimator : 982/1225 [00:03<00:00, 316.87it/s]
81%|████████ | Scoring GeneralizingEstimator : 992/1225 [00:03<00:00, 315.80it/s]
82%|████████▏ | Scoring GeneralizingEstimator : 1003/1225 [00:03<00:00, 313.15it/s]
83%|████████▎ | Scoring GeneralizingEstimator : 1015/1225 [00:03<00:00, 315.18it/s]
84%|████████▍ | Scoring GeneralizingEstimator : 1029/1225 [00:03<00:00, 320.09it/s]
85%|████████▍ | Scoring GeneralizingEstimator : 1040/1225 [00:03<00:00, 320.15it/s]
85%|████████▌ | Scoring GeneralizingEstimator : 1047/1225 [00:03<00:00, 312.67it/s]
86%|████████▌ | Scoring GeneralizingEstimator : 1056/1225 [00:03<00:00, 310.34it/s]
87%|████████▋ | Scoring GeneralizingEstimator : 1064/1225 [00:03<00:00, 306.73it/s]
88%|████████▊ | Scoring GeneralizingEstimator : 1072/1225 [00:03<00:00, 303.24it/s]
88%|████████▊ | Scoring GeneralizingEstimator : 1081/1225 [00:03<00:00, 301.37it/s]
89%|████████▉ | Scoring GeneralizingEstimator : 1092/1225 [00:03<00:00, 302.51it/s]
90%|████████▉ | Scoring GeneralizingEstimator : 1102/1225 [00:03<00:00, 302.13it/s]
91%|█████████ | Scoring GeneralizingEstimator : 1113/1225 [00:03<00:00, 303.21it/s]
92%|█████████▏| Scoring GeneralizingEstimator : 1123/1225 [00:03<00:00, 302.39it/s]
93%|█████████▎| Scoring GeneralizingEstimator : 1134/1225 [00:03<00:00, 303.11it/s]
93%|█████████▎| Scoring GeneralizingEstimator : 1143/1225 [00:03<00:00, 301.10it/s]
94%|█████████▍| Scoring GeneralizingEstimator : 1152/1225 [00:03<00:00, 299.33it/s]
95%|█████████▍| Scoring GeneralizingEstimator : 1163/1225 [00:03<00:00, 300.34it/s]
96%|█████████▌| Scoring GeneralizingEstimator : 1173/1225 [00:03<00:00, 300.11it/s]
96%|█████████▋| Scoring GeneralizingEstimator : 1182/1225 [00:04<00:00, 298.08it/s]
97%|█████████▋| Scoring GeneralizingEstimator : 1191/1225 [00:04<00:00, 296.39it/s]
98%|█████████▊| Scoring GeneralizingEstimator : 1204/1225 [00:04<00:00, 300.44it/s]
99%|█████████▉| Scoring GeneralizingEstimator : 1214/1225 [00:04<00:00, 300.21it/s]
100%|█████████▉| Scoring GeneralizingEstimator : 1224/1225 [00:04<00:00, 300.01it/s]
100%|██████████| Scoring GeneralizingEstimator : 1225/1225 [00:04<00:00, 295.62it/s]
Plot
fig, ax = plt.subplots(layout="constrained")
im = ax.matshow(
scores,
vmin=0,
vmax=1.0,
cmap="RdBu_r",
origin="lower",
extent=epochs.times[[0, -1, 0, -1]],
)
ax.axhline(0.0, color="k")
ax.axvline(0.0, color="k")
ax.xaxis.set_ticks_position("bottom")
ax.set_xlabel(
'Condition: "Right"\nTesting Time (s)',
)
ax.set_ylabel('Condition: "Left"\nTraining Time (s)')
ax.set_title("Generalization across time and condition", fontweight="bold")
fig.colorbar(im, ax=ax, label="Performance (ROC AUC)")
plt.show()
References#
Total running time of the script: (0 minutes 9.800 seconds)
Estimated memory usage: 141 MB