Compute spatial filters with Spatio-Spectral Decomposition (SSD)#

In this example, we will compute spatial filters for retaining oscillatory brain activity and down-weighting 1/f background signals as proposed by [1]. The idea is to learn spatial filters that separate oscillatory dynamics from surrounding non-oscillatory noise based on the covariance in the frequency band of interest and the noise covariance based on surrounding frequencies.

# Author: Denis A. Engemann <denis.engemann@gmail.com>
#         Victoria Peterson <victoriapeterson09@gmail.com>
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt

import mne
from mne import Epochs
from mne.datasets.fieldtrip_cmc import data_path
from mne.decoding import SSD

Define parameters

fname = data_path() / "SubjectCMC.ds"

# Prepare data
raw = mne.io.read_raw_ctf(fname)
raw.crop(tmin=50.0, tmax=110.0).load_data()  # crop for memory purposes
raw.resample(sfreq=250)

raw.pick_types(meg=True, ref_meg=False)

freqs_sig = 9, 12
freqs_noise = 8, 13


ssd = SSD(
    info=raw.info,
    reg="oas",
    sort_by_spectral_ratio=False,  # False for purpose of example.
    filt_params_signal=dict(
        l_freq=freqs_sig[0],
        h_freq=freqs_sig[1],
        l_trans_bandwidth=1,
        h_trans_bandwidth=1,
    ),
    filt_params_noise=dict(
        l_freq=freqs_noise[0],
        h_freq=freqs_noise[1],
        l_trans_bandwidth=1,
        h_trans_bandwidth=1,
    ),
)
ssd.fit(X=raw.get_data())

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 ... 72000  =      0.000 ...    60.000 secs...
29 events found on stim channel STIM
Event IDs: [   196608    262144    327680    393216    458752  67108864  67174400
 134742016 136314880 268435456]
29 events found on stim channel STIM
Event IDs: [   196608    262144    327680    393216    458752  67108864  67174400
 134742016 136314880 268435456]
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Removing 5 compensators from info because not all compensation channels were picked.
Setting up band-pass filter from 9 - 12 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: 9.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz)
- Upper passband edge: 12.00 Hz
- Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz)
- Filter length: 825 samples (3.300 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.0s
Setting up band-pass filter from 8 - 13 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: 8.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 7.50 Hz)
- Upper passband edge: 13.00 Hz
- Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz)
- Filter length: 825 samples (3.300 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.0s
Estimating covariance using OAS
Done.
Estimating covariance using OAS
Done.
Computing rank from covariance with rank=None
    Using tolerance 1.2e-14 (2.2e-16 eps * 151 dim * 0.37  max singular value)
    Estimated rank (mag): 151
    MAG: rank 151 computed from 151 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.9e-15 (2.2e-16 eps * 151 dim * 0.086  max singular value)
    Estimated rank (mag): 151
    MAG: rank 151 computed from 151 data channels with 0 projectors
Preserving covariance rank (151)
Done.

SSD(filt_params_noise={'h_freq': 13, 'h_trans_bandwidth': 1, 'l_freq': 8,
                       'l_trans_bandwidth': 1},
    filt_params_signal={'h_freq': 12, 'h_trans_bandwidth': 1, 'l_freq': 9,
                        'l_trans_bandwidth': 1},
    info=<Info | 14 non-empty values
 bads: []
 ch_names: MLC11-1706, MLC12-1706, MLC13-1706, MLC14-1706, MLC15-1706, ...
 chs: 151 Magnetometers
 ctf_head_t: CTF/4D/KIT head -> head transform
 custom_re...
 dev_ctf_t: MEG device -> CTF/4D/KIT head transform
 dev_head_t: MEG device -> head transform
 dig: 7 items (3 Cardinal, 4 EEG)
 highpass: 0.0 Hz
 hpi_results: 1 item (list)
 lowpass: 125.0 Hz
 meas_date: 2005-01-31 14:43:00 UTC
 meas_id: 4 items (dict)
 nchan: 151
 projs: []
 sfreq: 250.0 Hz
 subject_info: <subject_info | his_id: Anonymized25263_1299153182_0>
>,
    n_fft=250, reg='oas', sort_by_spectral_ratio=False)

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Let’s investigate spatial filter with the max power ratio. We will first inspect the topographies. According to Nikulin et al. (2011), this is done by either inverting the filters (W^{-1}) or by multiplying the noise cov with the filters Eq. (22) (C_n W)^t. We rely on the inversion approach here.

pattern = mne.EvokedArray(data=ssd.patterns_[:4].T, info=ssd.info)
pattern.plot_topomap(units=dict(mag="A.U."), time_format="")

# The topographies suggest that we picked up a parietal alpha generator.

# Transform
ssd_sources = ssd.transform(X=raw.get_data())

# Get psd of SSD-filtered signals.
psd, freqs = mne.time_frequency.psd_array_welch(
    ssd_sources, sfreq=raw.info["sfreq"], n_fft=4096
)

# Get spec_ratio information (already sorted).
# Note that this is not necessary if sort_by_spectral_ratio=True (default).
spec_ratio, sorter = ssd.get_spectral_ratio(ssd_sources)

# Plot spectral ratio (see Eq. 24 in Nikulin et al., 2011).
fig, ax = plt.subplots(1)
ax.plot(spec_ratio, color="black")
ax.plot(spec_ratio[sorter], color="orange", label="sorted eigenvalues")
ax.set_xlabel("Eigenvalue Index")
ax.set_ylabel(r"Spectral Ratio $\frac{P_f}{P_{sf}}$")
ax.legend()
ax.axhline(1, linestyle="--")

# We can see that the initial sorting based on the eigenvalues
# was already quite good. However, when using few components only
# the sorting might make a difference.

Effective window size : 16.384 (s)
Effective window size : 1.000 (s)

Let’s also look at the power spectrum of that source and compare it to the power spectrum of the source with lowest SNR.

below50 = freqs < 50
# for highlighting the freq. band of interest
bandfilt = (freqs_sig[0] <= freqs) & (freqs <= freqs_sig[1])
fig, ax = plt.subplots(1)
ax.loglog(freqs[below50], psd[0, below50], label="max SNR")
ax.loglog(freqs[below50], psd[-1, below50], label="min SNR")
ax.loglog(freqs[below50], psd[:, below50].mean(axis=0), label="mean")
ax.fill_between(freqs[bandfilt], 0, 10000, color="green", alpha=0.15)
ax.set_xlabel("log(frequency)")
ax.set_ylabel("log(power)")
ax.legend()

# We can clearly see that the selected component enjoys an SNR that is
# way above the average power spectrum.

Epoched data#

Although we suggest using this method before epoching, there might be some situations in which data can only be treated by chunks.

# Build epochs as sliding windows over the continuous raw file.
events = mne.make_fixed_length_events(raw, id=1, duration=5.0, overlap=0.0)

# Epoch length is 5 seconds.
epochs = Epochs(raw, events, tmin=0.0, tmax=5, baseline=None, preload=True)

ssd_epochs = SSD(
    info=epochs.info,
    reg="oas",
    filt_params_signal=dict(
        l_freq=freqs_sig[0],
        h_freq=freqs_sig[1],
        l_trans_bandwidth=1,
        h_trans_bandwidth=1,
    ),
    filt_params_noise=dict(
        l_freq=freqs_noise[0],
        h_freq=freqs_noise[1],
        l_trans_bandwidth=1,
        h_trans_bandwidth=1,
    ),
)
ssd_epochs.fit(X=epochs.get_data(copy=False))

# Plot topographies.
pattern_epochs = mne.EvokedArray(data=ssd_epochs.patterns_[:4].T, info=ssd_epochs.info)
pattern_epochs.plot_topomap(units=dict(mag="A.U."), time_format="")

Not setting metadata
12 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 12 events and 1251 original time points ...
1 bad epochs dropped
Setting up band-pass filter from 9 - 12 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: 9.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 8.50 Hz)
- Upper passband edge: 12.00 Hz
- Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 12.50 Hz)
- Filter length: 825 samples (3.300 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 287 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 449 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 647 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 881 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 1151 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 1457 tasks      | elapsed:    0.2s
Setting up band-pass filter from 8 - 13 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: 8.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 7.50 Hz)
- Upper passband edge: 13.00 Hz
- Upper transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 13.50 Hz)
- Filter length: 825 samples (3.300 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 287 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 449 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 647 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 881 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 1151 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 1457 tasks      | elapsed:    0.2s
Estimating covariance using OAS
Done.
Estimating covariance using OAS
Done.
Computing rank from covariance with rank=None
    Using tolerance 1.3e-14 (2.2e-16 eps * 151 dim * 0.38  max singular value)
    Estimated rank (mag): 151
    MAG: rank 151 computed from 151 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 3e-15 (2.2e-16 eps * 151 dim * 0.09  max singular value)
    Estimated rank (mag): 151
    MAG: rank 151 computed from 151 data channels with 0 projectors
Preserving covariance rank (151)
Effective window size : 1.000 (s)
Done.

References#

Total running time of the script: (0 minutes 3.983 seconds)

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