EEG speech envelope TRF¶

Analyze continuous speech data from the mTRF dataset [1]: use the boosting algorithm for estimating temporal response functions (TRFs) to the acoustic envelope.

# Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
# sphinx_gallery_thumbnail_number = 4
import os

from scipy.io import loadmat
import mne
from eelbrain import *

# Load the mTRF speech dataset and convert data to NDVars
root = mne.datasets.mtrf.data_path()
speech_path = os.path.join(root, 'speech_data.mat')
mdata = loadmat(speech_path)

# Time axis
tstep = 1. / mdata['Fs'][0, 0]
n_times = mdata['envelope'].shape[0]
time = UTS(0, tstep, n_times)
# Load the EEG sensor coordinates (drop fiducials coordinates, which are stored
# after sensor 128)
sensor = Sensor.from_montage('biosemi128')[:128]
# Frequency dimension for the spectrogram
band = Scalar('frequency', range(16))
# Create variables
envelope = NDVar(mdata['envelope'][:, 0], (time,), name='envelope')
eeg = NDVar(mdata['EEG'], (time, sensor), name='EEG', info={'unit': 'µV'})
spectrogram = NDVar(mdata['spectrogram'], (time, band), name='spectrogram')
# Exclude a bad channel
eeg = eeg[sensor.index(exclude='A13')]

Data¶

Plot the spectrogram of the speech stimulus:

plot.Array(spectrogram, xlim=5, w=6, h=2)

Out:

<Array: spectrogram>

Plot the envelope used as stimulus representation for reverse correlation:

plot.UTS(envelope, xlim=5, w=6, h=2)

Out:

<UTS: envelope>

Plot the corresponding EEG data:

p = plot.TopoButterfly(eeg, xlim=5, w=7, h=2)
p.set_time(1.200)

Reverse correlation¶

TRF for the envelope using boosting:

TRF from -100 to 400 ms
Basis of 100 ms Hamming windows
Use 4 partitionings of the data for cross-validation based early stopping

res = boosting(eeg, envelope, -0.100, 0.400, basis=0.100, partitions=4)
p = plot.TopoButterfly(res.h_scaled, w=6, h=2)
p.set_time(.180)

Out:

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Multiple predictors¶

Multiple predictors additively explain the signal:

# Derive acoustic onsets from the envelope
onset = envelope.diff('time', name='onset').clip(0)
onset *= envelope.max() / onset.max()
plot.UTS([[envelope, onset]], xlim=5, w=6, h=2)

Out:

<UTS: envelope, onset>

res_onset = boosting(eeg, [onset, envelope], -0.100, 0.400, basis=0.100, partitions=4)
p = plot.TopoButterfly(res_onset.h_scaled, w=6, h=3)
p.set_time(.150)

Out:

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Boosting 127 signals:  94%|#########3| 475/508 [00:17<00:01, 22.58it/s]
Boosting 127 signals:  94%|#########4| 479/508 [00:17<00:01, 24.40it/s]
Boosting 127 signals:  95%|#########4| 482/508 [00:17<00:01, 24.77it/s]
Boosting 127 signals:  95%|#########5| 485/508 [00:17<00:01, 22.43it/s]
Boosting 127 signals:  96%|#########6| 488/508 [00:17<00:00, 21.88it/s]
Boosting 127 signals:  97%|#########6| 491/508 [00:17<00:00, 20.27it/s]
Boosting 127 signals:  97%|#########7| 495/508 [00:17<00:00, 22.84it/s]
Boosting 127 signals:  98%|#########8| 499/508 [00:18<00:00, 25.53it/s]
Boosting 127 signals:  99%|#########8| 502/508 [00:18<00:00, 25.28it/s]
Boosting 127 signals:  99%|#########9| 505/508 [00:18<00:00, 22.97it/s]
Boosting 127 signals: 100%|##########| 508/508 [00:18<00:00, 27.56it/s]

Compare models¶

Compare model quality through the correlation between measured and predicted responses:

plot.Topomap([res.r, res_onset.r], w=4, h=2, ncol=2, axtitle=['envelope', 'envelope + onset'])

Out:

<Topomap: correlation>

References¶

[1]	Crosse, M. J., Liberto, D., M, G., Bednar, A., & Lalor, E. C. (2016). The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00604

Total running time of the script: ( 0 minutes 31.863 seconds)

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