Time-frequency beamforming using DICS

Compute DICS source power [1] in a grid of time-frequency windows and display results.

References

[1]Dalal et al. Five-dimensional neuroimaging: Localization of the time-frequency dynamics of cortical activity. NeuroImage (2008) vol. 40 (4) pp. 1686-1700
# Author: Roman Goj <roman.goj@gmail.com>
#
# License: BSD (3-clause)

import mne
from mne.event import make_fixed_length_events
from mne.datasets import sample
from mne.time_frequency import csd_fourier
from mne.beamformer import tf_dics
from mne.viz import plot_source_spectrogram

print(__doc__)

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
noise_fname = data_path + '/MEG/sample/ernoise_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
subjects_dir = data_path + '/subjects'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name

Read raw data

raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.info['bads'] = ['MEG 2443']  # 1 bad MEG channel

# Pick a selection of magnetometer channels. A subset of all channels was used
# to speed up the example. For a solution based on all MEG channels use
# meg=True, selection=None and add mag=4e-12 to the reject dictionary.
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg='mag', eeg=False, eog=False,
                       stim=False, exclude='bads',
                       selection=left_temporal_channels)
raw.pick_channels([raw.ch_names[pick] for pick in picks])
reject = dict(mag=4e-12)
# Re-normalize our empty-room projectors, which should be fine after
# subselection
raw.info.normalize_proj()

# Setting time windows. Note that tmin and tmax are set so that time-frequency
# beamforming will be performed for a wider range of time points than will
# later be displayed on the final spectrogram. This ensures that all time bins
# displayed represent an average of an equal number of time windows.
tmin, tmax, tstep = -0.5, 0.75, 0.05  # s
tmin_plot, tmax_plot = -0.3, 0.5  # s

# Read epochs
event_id = 1
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                    baseline=None, preload=True, proj=True, reject=reject)

# Read empty room noise raw data
raw_noise = mne.io.read_raw_fif(noise_fname, preload=True)
raw_noise.info['bads'] = ['MEG 2443']  # 1 bad MEG channel
raw_noise.pick_channels([raw_noise.ch_names[pick] for pick in picks])
raw_noise.info.normalize_proj()

# Create noise epochs and make sure the number of noise epochs corresponds to
# the number of data epochs
events_noise = make_fixed_length_events(raw_noise, event_id)
epochs_noise = mne.Epochs(raw_noise, events_noise, event_id, tmin_plot,
                          tmax_plot, baseline=None, preload=True, proj=True,
                          reject=reject)
epochs_noise.info.normalize_proj()
epochs_noise.apply_proj()
# then make sure the number of epochs is the same
epochs_noise = epochs_noise[:len(epochs.events)]

# Read forward operator
forward = mne.read_forward_solution(fname_fwd)

# Read label
label = mne.read_label(fname_label)

Out:

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Current compensation grade : 0
Reading 0 ... 166799  =      0.000 ...   277.714 secs...
72 matching events found
No baseline correction applied
Not setting metadata
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 72 events and 751 original time points ...
0 bad epochs dropped
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 19800 ... 85867 =     32.966 ...   142.965 secs
Ready.
Current compensation grade : 0
Reading 0 ... 66067  =      0.000 ...   109.999 secs...
110 matching events found
No baseline correction applied
Not setting metadata
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 110 events and 481 original time points ...
1 bad epochs dropped
Projections have already been applied. Setting proj attribute to True.
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    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
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame

Time-frequency beamforming based on DICS

# Setting frequency bins as in Dalal et al. 2008
freq_bins = [(4, 12), (12, 30), (30, 55), (65, 300)]  # Hz
win_lengths = [0.3, 0.2, 0.15, 0.1]  # s
# Then set FFTs length for each frequency range.
# Should be a power of 2 to be faster.
n_ffts = [256, 128, 128, 128]

# Subtract evoked response prior to computation?
subtract_evoked = False

# Calculating noise cross-spectral density from empty room noise for each
# frequency bin and the corresponding time window length. To calculate noise
# from the baseline period in the data, change epochs_noise to epochs
noise_csds = []
for freq_bin, win_length, n_fft in zip(freq_bins, win_lengths, n_ffts):
    noise_csd = csd_fourier(epochs_noise, fmin=freq_bin[0], fmax=freq_bin[1],
                            tmin=-win_length, tmax=0, n_fft=n_fft)
    noise_csds.append(noise_csd.sum())

# Computing DICS solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
               freq_bins=freq_bins, subtract_evoked=subtract_evoked,
               n_ffts=n_ffts, reg=0.05, label=label, inversion='matrix')

# Plotting source spectrogram for source with maximum activity
# Note that tmin and tmax are set to display a time range that is smaller than
# the one for which beamforming estimates were calculated. This ensures that
# all time bins shown are a result of smoothing across an identical number of
# time windows.
plot_source_spectrogram(stcs, freq_bins, tmin=tmin_plot, tmax=tmax_plot,
                        source_index=None, colorbar=True)
../../_images/sphx_glr_plot_tf_dics_001.png

Out:

Computing cross-spectral density from epochs...
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[done]
Computing time-frequency DICS beamformer for time window -500 to -200 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -450 to -150 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -400 to -100 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -350 to -49 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -300 to 0 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -250 to 49 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -199 to 100 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -149 to 150 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -99 to 200 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -49 to 250 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 0 to 300 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 50 to 350 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 100 to 400 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 150 to 450 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 200 to 500 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window 250 to 550 ms, in frequency range 4 to 12 Hz
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Computing time-frequency DICS beamformer for time window 350 to 650 ms, in frequency range 4 to 12 Hz
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Computing time-frequency DICS beamformer for time window 450 to 750 ms, in frequency range 4 to 12 Hz
Computing time-frequency DICS beamformer for time window -500 to -300 ms, in frequency range 12 to 30 Hz
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Computing time-frequency DICS beamformer for time window 500 to 650 ms, in frequency range 30 to 55 Hz
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Computing time-frequency DICS beamformer for time window -500 to -400 ms, in frequency range 65 to 300 Hz
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Computing time-frequency DICS beamformer for time window -199 to -99 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window -149 to -49 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window -99 to 0 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window -49 to 50 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 0 to 100 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 50 to 150 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 100 to 200 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 150 to 250 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 200 to 300 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 250 to 350 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 300 to 400 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 350 to 450 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 400 to 500 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 450 to 550 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 500 to 600 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 550 to 650 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 600 to 700 ms, in frequency range 65 to 300 Hz
Computing time-frequency DICS beamformer for time window 650 to 750 ms, in frequency range 65 to 300 Hz

Total running time of the script: ( 0 minutes 34.376 seconds)

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