Note
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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, 22.29it/s]
9%|8 | Fitting GeneralizingEstimator : 3/35 [00:00<00:01, 24.64it/s]
17%|#7 | Fitting GeneralizingEstimator : 6/35 [00:00<00:00, 36.78it/s]
23%|##2 | Fitting GeneralizingEstimator : 8/35 [00:00<00:00, 31.38it/s]
34%|###4 | Fitting GeneralizingEstimator : 12/35 [00:00<00:00, 33.90it/s]
37%|###7 | Fitting GeneralizingEstimator : 13/35 [00:00<00:00, 30.68it/s]
49%|####8 | Fitting GeneralizingEstimator : 17/35 [00:00<00:00, 37.43it/s]
57%|#####7 | Fitting GeneralizingEstimator : 20/35 [00:00<00:00, 36.54it/s]
63%|######2 | Fitting GeneralizingEstimator : 22/35 [00:00<00:00, 34.99it/s]
69%|######8 | Fitting GeneralizingEstimator : 24/35 [00:00<00:00, 33.08it/s]
77%|#######7 | Fitting GeneralizingEstimator : 27/35 [00:00<00:00, 36.07it/s]
80%|######## | Fitting GeneralizingEstimator : 28/35 [00:00<00:00, 33.90it/s]
89%|########8 | Fitting GeneralizingEstimator : 31/35 [00:00<00:00, 36.26it/s]
91%|#########1| Fitting GeneralizingEstimator : 32/35 [00:00<00:00, 34.11it/s]
97%|#########7| Fitting GeneralizingEstimator : 34/35 [00:01<00:00, 32.84it/s]
100%|##########| Fitting GeneralizingEstimator : 35/35 [00:01<00:00, 33.96it/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]
2%|1 | Scoring GeneralizingEstimator : 22/1225 [00:00<00:01, 641.37it/s]
3%|2 | Scoring GeneralizingEstimator : 33/1225 [00:00<00:02, 480.78it/s]
5%|4 | Scoring GeneralizingEstimator : 57/1225 [00:00<00:02, 560.23it/s]
5%|5 | Scoring GeneralizingEstimator : 62/1225 [00:00<00:03, 380.52it/s]
7%|7 | Scoring GeneralizingEstimator : 86/1225 [00:00<00:02, 443.34it/s]
8%|7 | Scoring GeneralizingEstimator : 97/1225 [00:00<00:03, 365.05it/s]
10%|9 | Scoring GeneralizingEstimator : 121/1225 [00:00<00:02, 409.87it/s]
11%|# | Scoring GeneralizingEstimator : 132/1225 [00:00<00:03, 358.32it/s]
13%|#2 | Scoring GeneralizingEstimator : 156/1225 [00:00<00:02, 394.27it/s]
14%|#3 | Scoring GeneralizingEstimator : 166/1225 [00:00<00:02, 384.50it/s]
14%|#3 | Scoring GeneralizingEstimator : 167/1225 [00:00<00:03, 350.78it/s]
16%|#5 | Scoring GeneralizingEstimator : 192/1225 [00:00<00:02, 385.39it/s]
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100%|##########| Scoring GeneralizingEstimator : 1225/1225 [00:03<00:00, 314.39it/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 10.708 seconds)
Estimated memory usage: 128 MB