Note

Click here to download the full example code

Decoding in time-frequency space data using the Common Spatial Pattern (CSP)

The time-frequency decomposition is estimated by iterating over raw data that has been band-passed at different frequencies. This is used to compute a covariance matrix over each epoch or a rolling time-window and extract the CSP filtered signals. A linear discriminant classifier is then applied to these signals.

# Authors: Laura Gwilliams <laura.gwilliams@nyu.edu>
#          Jean-Remi King <jeanremi.king@gmail.com>
#          Alex Barachant <alexandre.barachant@gmail.com>
#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

from mne import Epochs, find_events, create_info
from mne.io import concatenate_raws, read_raw_edf
from mne.datasets import eegbci
from mne.decoding import CSP
from mne.time_frequency import AverageTFR

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder

Set parameters and read data

event_id = dict(hands=2, feet=3)  # motor imagery: hands vs feet
subject = 1
runs = [6, 10, 14]
raw_fnames = eegbci.load_data(subject, runs)
raw_files = [read_raw_edf(f, stim_channel='auto', preload=True)
             for f in raw_fnames]
raw = concatenate_raws(raw_files)

# Extract information from the raw file
sfreq = raw.info['sfreq']
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
raw.pick_types(meg=False, eeg=True, stim=False, eog=False, exclude='bads')

# Assemble the classifier using scikit-learn pipeline
clf = make_pipeline(CSP(n_components=4, reg=None, log=True, norm_trace=False),
                    LinearDiscriminantAnalysis())
n_splits = 5  # how many folds to use for cross-validation
cv = StratifiedKFold(n_splits=n_splits, shuffle=True)

# Classification & Time-frequency parameters
tmin, tmax = -.200, 2.000
n_cycles = 10.  # how many complete cycles: used to define window size
min_freq = 5.
max_freq = 25.
n_freqs = 8  # how many frequency bins to use

# Assemble list of frequency range tuples
freqs = np.linspace(min_freq, max_freq, n_freqs)  # assemble frequencies
freq_ranges = list(zip(freqs[:-1], freqs[1:]))  # make freqs list of tuples

# Infer window spacing from the max freq and number of cycles to avoid gaps
window_spacing = (n_cycles / np.max(freqs) / 2.)
centered_w_times = np.arange(tmin, tmax, window_spacing)[1:]
n_windows = len(centered_w_times)

# Instantiate label encoder
le = LabelEncoder()

Out:

Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R06.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R10.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R14.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Trigger channel has a non-zero initial value of 1 (consider using initial_event=True to detect this event)
Removing orphaned offset at the beginning of the file.
89 events found
Event IDs: [1 2 3]

Loop through frequencies, apply classifier and save scores

# init scores
freq_scores = np.zeros((n_freqs - 1,))

# Loop through each frequency range of interest
for freq, (fmin, fmax) in enumerate(freq_ranges):

    # Infer window size based on the frequency being used
    w_size = n_cycles / ((fmax + fmin) / 2.)  # in seconds

    # Apply band-pass filter to isolate the specified frequencies
    raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin',
                                   skip_by_annotation='edge')

    # Extract epochs from filtered data, padded by window size
    epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size,
                    proj=False, baseline=None, preload=True)
    epochs.drop_bad()
    y = le.fit_transform(epochs.events[:, 2])

    X = epochs.get_data()

    # Save mean scores over folds for each frequency and time window
    freq_scores[freq] = np.mean(cross_val_score(estimator=clf, X=X, y=y,
                                                scoring='roc_auc', cv=cv,
                                                n_jobs=1), axis=0)

Out:

Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 851 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 697 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 617 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 567 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 533 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 507 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 489 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Plot frequency results

plt.bar(freqs[:-1], freq_scores, width=np.diff(freqs)[0],
        align='edge', edgecolor='black')
plt.xticks(freqs)
plt.ylim([0, 1])
plt.axhline(len(epochs['feet']) / len(epochs), color='k', linestyle='--',
            label='chance level')
plt.legend()
plt.xlabel('Frequency (Hz)')
plt.ylabel('Decoding Scores')
plt.title('Frequency Decoding Scores')
../../_images/sphx_glr_plot_decoding_csp_timefreq_001.png

Loop through frequencies and time, apply classifier and save scores

# init scores
tf_scores = np.zeros((n_freqs - 1, n_windows))

# Loop through each frequency range of interest
for freq, (fmin, fmax) in enumerate(freq_ranges):

    # Infer window size based on the frequency being used
    w_size = n_cycles / ((fmax + fmin) / 2.)  # in seconds

    # Apply band-pass filter to isolate the specified frequencies
    raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin',
                                   skip_by_annotation='edge')

    # Extract epochs from filtered data, padded by window size
    epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size,
                    proj=False, baseline=None, preload=True)
    epochs.drop_bad()
    y = le.fit_transform(epochs.events[:, 2])

    # Roll covariance, csp and lda over time
    for t, w_time in enumerate(centered_w_times):

        # Center the min and max of the window
        w_tmin = w_time - w_size / 2.
        w_tmax = w_time + w_size / 2.

        # Crop data into time-window of interest
        X = epochs.copy().crop(w_tmin, w_tmax).get_data()

        # Save mean scores over folds for each frequency and time window
        tf_scores[freq, t] = np.mean(cross_val_score(estimator=clf, X=X, y=y,
                                                     scoring='roc_auc', cv=cv,
                                                     n_jobs=1), axis=0)

Out:

Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 5 - 7.9 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.0 Hz
Filter length of 265 samples (1.656 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 851 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
Done.
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Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
Setting up band-pass filter from 7.9 - 11 Hz
l_trans_bandwidth chosen to be 2.0 Hz
h_trans_bandwidth chosen to be 2.7 Hz
Filter length of 265 samples (1.656 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 697 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
Setting up band-pass filter from 11 - 14 Hz
l_trans_bandwidth chosen to be 2.7 Hz
h_trans_bandwidth chosen to be 3.4 Hz
Filter length of 197 samples (1.231 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 617 original time points ...
0 bad epochs dropped
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Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
Setting up band-pass filter from 14 - 16 Hz
l_trans_bandwidth chosen to be 3.4 Hz
h_trans_bandwidth chosen to be 4.1 Hz
Filter length of 157 samples (0.981 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 567 original time points ...
0 bad epochs dropped
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Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
Setting up band-pass filter from 16 - 19 Hz
l_trans_bandwidth chosen to be 4.1 Hz
h_trans_bandwidth chosen to be 4.8 Hz
Filter length of 129 samples (0.806 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 533 original time points ...
0 bad epochs dropped
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Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
Setting up band-pass filter from 19 - 22 Hz
l_trans_bandwidth chosen to be 4.8 Hz
h_trans_bandwidth chosen to be 5.5 Hz
Filter length of 111 samples (0.694 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 507 original time points ...
0 bad epochs dropped
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Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
Setting up band-pass filter from 22 - 25 Hz
l_trans_bandwidth chosen to be 5.5 Hz
h_trans_bandwidth chosen to be 6.2 Hz
Filter length of 95 samples (0.594 sec) selected
45 matching events found
No baseline correction applied
Not setting metadata
Loading data for 45 events and 489 original time points ...
0 bad epochs dropped
Estimating covariance using EMPIRICAL
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Estimating covariance using EMPIRICAL
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Plot time-frequency results

# Set up time frequency object
av_tfr = AverageTFR(create_info(['freq'], sfreq), tf_scores[np.newaxis, :],
                    centered_w_times, freqs[1:], 1)

chance = np.mean(y)  # set chance level to white in the plot
av_tfr.plot([0], vmin=chance, title="Time-Frequency Decoding Scores",
            cmap=plt.cm.Reds)
../../_images/sphx_glr_plot_decoding_csp_timefreq_002.png

Out:

No baseline correction applied

Total running time of the script: ( 1 minutes 11.696 seconds)

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