Note

Go to the end to download the full example code.

Continuous Target Decoding with SPoC#

Source Power Comodulation (SPoC) [1] allows to identify the composition of orthogonal spatial filters that maximally correlate with a continuous target.

SPoC can be seen as an extension of the CSP for continuous variables.

Here, SPoC is applied to decode the (continuous) fluctuation of an electromyogram from MEG beta activity using data from Cortico-Muscular Coherence example of FieldTrip

# Author: Alexandre Barachant <alexandre.barachant@gmail.com>
# Jean-Rémi King <jeanremi.king@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

importmatplotlib.pyplotasplt
fromsklearn.linear_modelimport Ridge
fromsklearn.model_selectionimport KFold , cross_val_predict
fromsklearn.pipelineimport make_pipeline
importmne
frommneimport Epochs
frommne.datasets.fieldtrip_cmcimport data_path
frommne.decodingimport SPoC , get_spatial_filter_from_estimator
# Define parameters
fname = data_path() / "SubjectCMC.ds"
raw = mne.io.read_raw_ctf (fname )
raw.crop(50.0, 200.0) # crop for memory purposes
# Filter muscular activity to only keep high frequencies
emg = raw.copy().pick(["EMGlft"]).load_data()
emg.filter(20.0, None)
# Filter MEG data to focus on beta band
raw.pick(picks=["meg", "ref_meg"]).load_data()
raw.filter(15.0, 30.0)
# Build epochs as sliding windows over the continuous raw file
events = mne.make_fixed_length_events (raw, id=1, duration=0.75)
# Epoch length is 1.5 second
meg_epochs = Epochs (raw, events , tmin=0.0, tmax=1.5, baseline=None, detrend=1, decim=12)
emg_epochs = Epochs (emg, events , tmin=0.0, tmax=1.5, baseline=None)
# Prepare classification
X = meg_epochs.get_data ()
y = emg_epochs.get_data ().var(axis=2)[:, 0] # target is EMG power
# Classification pipeline with SPoC spatial filtering and Ridge Regression
spoc = SPoC (n_components=2, log=True, reg="oas", rank="full")
clf = make_pipeline (spoc , Ridge ())
# Define a two fold cross-validation
cv = KFold (n_splits=2, shuffle=False)
# Run cross validation
y_preds = cross_val_predict (clf , X , y , cv =cv )
# Plot the True EMG power and the EMG power predicted from MEG data
fig , ax = plt.subplots (1, 1, figsize=[10, 4], layout="constrained")
times = raw.times [meg_epochs.events [:, 0] - raw.first_samp]
ax.plot (times , y_preds , color="b", label="Predicted EMG")
ax.plot (times , y , color="r", label="True EMG")
ax.set_xlabel ("Time (s)")
ax.set_ylabel ("EMG Power")
ax.set_title ("SPoC MEG Predictions")
plt.legend ()
plt.show ()

SPoC MEG Predictions

ds directory : /home/circleci/mne_data/MNE-fieldtrip_cmc-data/SubjectCMC.ds
 res4 data read.
 hc data read.
 Separate EEG position data file not present.
 Quaternion matching (desired vs. transformed):
 0.33 78.32 0.00 mm <-> 0.33 78.32 0.00 mm (orig : -71.62 40.46 -256.48 mm) diff = 0.000 mm
 -0.33 -78.32 -0.00 mm <-> -0.33 -78.32 -0.00 mm (orig : 39.27 -70.16 -258.60 mm) diff = 0.000 mm
 114.65 0.00 -0.00 mm <-> 114.65 0.00 -0.00 mm (orig : 64.35 66.64 -262.01 mm) diff = 0.000 mm
 Coordinate transformations established.
 Polhemus data for 3 HPI coils added
 Device coordinate locations for 3 HPI coils added
Picked positions of 4 EEG channels from channel info
 4 EEG locations added to Polhemus data.
 Measurement info composed.
Finding samples for /home/circleci/mne_data/MNE-fieldtrip_cmc-data/SubjectCMC.ds/SubjectCMC.meg4:
 System clock channel is available, checking which samples are valid.
 75 x 12000 = 911610 samples from 191 chs
 390 samples omitted at the end
Current compensation grade : 0
Reading 0 ... 180000 = 0.000 ... 150.000 secs...
Filtering raw data in 1 contiguous segment
Setting up high-pass filter at 20 Hz
FIR filter parameters
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Designing a one-pass, zero-phase, non-causal highpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 20.00
- Lower transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 17.50 Hz)
- Filter length: 793 samples (0.661 s)
Reading 0 ... 180000 = 0.000 ... 150.000 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 15 - 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: 15.00
- Lower transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 13.12 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 1057 samples (0.881 s)
Not setting metadata
200 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
200 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 200 events and 1801 original time points (prior to decimation) ...
1 bad epochs dropped
Using data from preloaded Raw for 200 events and 1801 original time points ...
1 bad epochs dropped
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Plot the contributions to the detected components (i.e., the forward model)

spoc.fit (X , y )
spf = get_spatial_filter_from_estimator (spoc , info=meg_epochs.info )
spf.plot_scree ()
# Plot patterns for the first three components
# with largest absolute generalized eigenvalues,
# as we can see on the scree plot
spf.plot_patterns (components=[0, 1, 2])

• Scree plot
• Pattern0, Pattern1, Pattern2, AU

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References#

[1 ]

Sven Dähne, Frank C. Meinecke, Stefan Haufe, Johannes Höhne, Michael Tangermann, Klaus-Robert Müller, and Vadim V. Nikulin. SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters. NeuroImage, 86:111–122, 2014. doi:10.1016/j.neuroimage.2013年07月07日9.

Total running time of the script: (0 minutes 20.013 seconds)

Download Jupyter notebook: decoding_spoc_CMC.ipynb

Download Python source code: decoding_spoc_CMC.py

Download zipped: decoding_spoc_CMC.zip

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