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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
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
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
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
events_fname = data_path + '/MEG/sample/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., 30., 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')
Out:
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 sec)
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='lbfgs'))
time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=1,
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(),
y=epochs['Left'].events[:, 2] > 2)
Out:
0%| | Fitting GeneralizingEstimator : 0/35 [00:00<?, ?it/s]
3%|2 | Fitting GeneralizingEstimator : 1/35 [00:00<00:01, 23.90it/s]
9%|8 | Fitting GeneralizingEstimator : 3/35 [00:00<00:01, 26.32it/s]
11%|#1 | Fitting GeneralizingEstimator : 4/35 [00:00<00:01, 27.07it/s]
14%|#4 | Fitting GeneralizingEstimator : 5/35 [00:00<00:01, 23.13it/s]
17%|#7 | Fitting GeneralizingEstimator : 6/35 [00:00<00:01, 23.95it/s]
20%|## | Fitting GeneralizingEstimator : 7/35 [00:00<00:01, 21.98it/s]
23%|##2 | Fitting GeneralizingEstimator : 8/35 [00:00<00:01, 22.63it/s]
26%|##5 | Fitting GeneralizingEstimator : 9/35 [00:00<00:01, 21.37it/s]
31%|###1 | Fitting GeneralizingEstimator : 11/35 [00:00<00:00, 24.82it/s]
34%|###4 | Fitting GeneralizingEstimator : 12/35 [00:00<00:00, 23.31it/s]
40%|#### | Fitting GeneralizingEstimator : 14/35 [00:00<00:00, 25.94it/s]
43%|####2 | Fitting GeneralizingEstimator : 15/35 [00:00<00:00, 24.61it/s]
49%|####8 | Fitting GeneralizingEstimator : 17/35 [00:00<00:00, 26.82it/s]
51%|#####1 | Fitting GeneralizingEstimator : 18/35 [00:00<00:00, 25.54it/s]
60%|###### | Fitting GeneralizingEstimator : 21/35 [00:00<00:00, 29.43it/s]
63%|######2 | Fitting GeneralizingEstimator : 22/35 [00:00<00:00, 27.89it/s]
66%|######5 | Fitting GeneralizingEstimator : 23/35 [00:00<00:00, 27.96it/s]
69%|######8 | Fitting GeneralizingEstimator : 24/35 [00:00<00:00, 26.60it/s]
71%|#######1 | Fitting GeneralizingEstimator : 25/35 [00:00<00:00, 26.76it/s]
74%|#######4 | Fitting GeneralizingEstimator : 26/35 [00:01<00:00, 25.54it/s]
77%|#######7 | Fitting GeneralizingEstimator : 27/35 [00:01<00:00, 25.74it/s]
80%|######## | Fitting GeneralizingEstimator : 28/35 [00:01<00:00, 24.76it/s]
83%|########2 | Fitting GeneralizingEstimator : 29/35 [00:01<00:00, 24.96it/s]
86%|########5 | Fitting GeneralizingEstimator : 30/35 [00:01<00:00, 24.09it/s]
89%|########8 | Fitting GeneralizingEstimator : 31/35 [00:01<00:00, 24.26it/s]
91%|#########1| Fitting GeneralizingEstimator : 32/35 [00:01<00:00, 23.54it/s]
97%|#########7| Fitting GeneralizingEstimator : 34/35 [00:01<00:00, 25.16it/s]
100%|##########| Fitting GeneralizingEstimator : 35/35 [00:01<00:00, 24.27it/s]
100%|##########| Fitting GeneralizingEstimator : 35/35 [00:01<00:00, 24.73it/s]
Score on the epochs where the stimulus was presented to the right.
scores = time_gen.score(X=epochs['Right'].get_data(),
y=epochs['Right'].events[:, 2] > 2)
Out:
0%| | Scoring GeneralizingEstimator : 0/1225 [00:00<?, ?it/s]
1%|1 | Scoring GeneralizingEstimator : 17/1225 [00:00<00:02, 497.22it/s]
3%|2 | Scoring GeneralizingEstimator : 35/1225 [00:00<00:02, 513.12it/s]
5%|4 | Scoring GeneralizingEstimator : 56/1225 [00:00<00:03, 378.00it/s]
7%|6 | Scoring GeneralizingEstimator : 80/1225 [00:00<00:02, 444.48it/s]
7%|7 | Scoring GeneralizingEstimator : 91/1225 [00:00<00:03, 363.14it/s]
9%|8 | Scoring GeneralizingEstimator : 108/1225 [00:00<00:02, 382.01it/s]
9%|9 | Scoring GeneralizingEstimator : 116/1225 [00:00<00:03, 321.67it/s]
11%|#1 | Scoring GeneralizingEstimator : 136/1225 [00:00<00:03, 349.85it/s]
12%|#1 | Scoring GeneralizingEstimator : 145/1225 [00:00<00:03, 314.53it/s]
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15%|#4 | Scoring GeneralizingEstimator : 179/1225 [00:00<00:03, 318.87it/s]
17%|#6 | Scoring GeneralizingEstimator : 203/1225 [00:00<00:02, 348.51it/s]
18%|#7 | Scoring GeneralizingEstimator : 216/1225 [00:00<00:03, 327.96it/s]
20%|#9 | Scoring GeneralizingEstimator : 240/1225 [00:00<00:02, 353.88it/s]
21%|## | Scoring GeneralizingEstimator : 252/1225 [00:00<00:02, 332.73it/s]
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100%|##########| Scoring GeneralizingEstimator : 1225/1225 [00:03<00:00, 308.76it/s]
Plot
fig, ax = plt.subplots(1)
im = ax.matshow(scores, vmin=0, vmax=1., cmap='RdBu_r', origin='lower',
extent=epochs.times[[0, -1, 0, -1]])
ax.axhline(0., color='k')
ax.axvline(0., color='k')
ax.xaxis.set_ticks_position('bottom')
ax.set_xlabel('Testing Time (s)')
ax.set_ylabel('Training Time (s)')
ax.set_title('Generalization across time and condition')
plt.colorbar(im, ax=ax)
plt.show()
References¶
- 1
Jean-Rémi King and Stanislas Dehaene. Characterizing the dynamics of mental representations: the temporal generalization method. Trends in Cognitive Sciences, 18(4):203–210, 2014. doi:10.1016/j.tics.2014.01.002.
Total running time of the script: ( 0 minutes 11.272 seconds)
Estimated memory usage: 129 MB