Brainstorm raw (median nerve) dataset¶

Here we compute the evoked from raw for the Brainstorm tutorial dataset. For comparison, see 1 and:

https://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf

References¶

1: Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience, vol. 2011, Article ID 879716, 13 pages, 2011. doi:10.1155/2011/879716

Out:

ds directory : /home/circleci/mne_data/MNE-brainstorm-data/bst_raw/MEG/bst_raw/subj001_somatosensory_20111109_01_AUX-f.ds
    res4 data read.
    hc data read.
    Separate EEG position data file not present.
    Quaternion matching (desired vs. transformed):
       0.84   69.49    0.00 mm <->    0.84   69.49   -0.00 mm (orig :  -44.30   51.45 -252.43 mm) diff =    0.000 mm
      -0.84  -69.49    0.00 mm <->   -0.84  -69.49   -0.00 mm (orig :   46.28  -53.58 -243.47 mm) diff =    0.000 mm
      86.41    0.00    0.00 mm <->   86.41    0.00    0.00 mm (orig :   63.60   55.82 -230.26 mm) diff =    0.000 mm
    Coordinate transformations established.
    Reading digitizer points from ['/home/circleci/mne_data/MNE-brainstorm-data/bst_raw/MEG/bst_raw/subj001_somatosensory_20111109_01_AUX-f.ds/subj00111092011.pos']...
    Polhemus data for 3 HPI coils added
    Device coordinate locations for 3 HPI coils added
Picked positions of 2 EEG channels from channel info
    2 EEG locations added to Polhemus data.
    Measurement info composed.
Finding samples for /home/circleci/mne_data/MNE-brainstorm-data/bst_raw/MEG/bst_raw/subj001_somatosensory_20111109_01_AUX-f.ds/subj001_somatosensory_20111109_01_AUX-f.meg4:
    System clock channel is available, checking which samples are valid.
    240 x 1800 = 432000 samples from 302 chs
Current compensation grade : 3
Reading 0 ... 216000  =      0.000 ...   180.000 secs...
Adding average EEG reference projection.
1 projection items deactivated
Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it.
Effective window size : 1.707 (s)
Effective window size : 1.707 (s)
Need more than one channel to make topography for eeg. Disabling interactivity.
Setting up band-stop filter

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandstop filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower transition bandwidth: 0.50 Hz
- Upper transition bandwidth: 0.50 Hz
- Filter length: 7921 samples (6.601 sec)

98 events found
Event IDs: [1 2]
Not setting metadata
Not setting metadata
53 matching events found
Applying baseline correction (mode: mean)
1 projection items activated
    Rejecting  epoch based on EOG : ['EEG058']
    Rejecting  epoch based on EOG : ['EEG058']
Removing projector <Projection | Average EEG reference, active : True, n_channels : 1>
No gradiometers found. Forcing n_grad to 0
No EEG channels found. Forcing n_eeg to 0
Adding projection: axial--0.100-0.000-PCA-01
Adding projection: axial--0.100-0.000-PCA-02
2 projection items deactivated
Created an SSP operator (subspace dimension = 2)
2 projection items activated
SSP projectors applied...

# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np

import mne
from mne.datasets.brainstorm import bst_raw
from mne.io import read_raw_ctf

print(__doc__)

tmin, tmax, event_id = -0.1, 0.3, 2  # take right-hand somato
reject = dict(mag=4e-12, eog=250e-6)

data_path = bst_raw.data_path()

raw_path = (data_path + '/MEG/bst_raw/' +
            'subj001_somatosensory_20111109_01_AUX-f.ds')
# Here we crop to half the length to save memory
raw = read_raw_ctf(raw_path).crop(0, 180).load_data()
raw.plot()

# set EOG channel
raw.set_channel_types({'EEG058': 'eog'})
raw.set_eeg_reference('average', projection=True)

# show power line interference and remove it
raw.plot_psd(tmax=60., average=False)
raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')

events = mne.find_events(raw, stim_channel='UPPT001')

# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
                       exclude='bads')

# Compute epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=reject, preload=False)

# compute evoked
evoked = epochs.average()

# remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline
evoked.add_proj(mne.compute_proj_evoked(evoked.copy().crop(tmax=0)))
evoked.apply_proj()

# fix stim artifact
mne.preprocessing.fix_stim_artifact(evoked)

# correct delays due to hardware (stim artifact is at 4 ms)
evoked.shift_time(-0.004)

# plot the result
evoked.plot(time_unit='s')

# show topomaps
evoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]),
                    time_unit='s')