Note
Click here to download the full example code
The MEGSIM consists of experimental and simulated MEG data which can be useful for reproducing research results.
The MEGSIM files will be dowloaded automatically.
The datasets are documented in: Aine CJ, Sanfratello L, Ranken D, Best E, MacArthur JA, Wallace T, Gilliam K, Donahue CH, Montano R, Bryant JE, Scott A, Stephen JM (2012) MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data. Neuroinformatics 10:141-158
Out:
851 events found
Event IDs: [ 2 3 5 9 17]
218 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on MAG : ['MEG 2311', 'MEG 2441']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 061', 'EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
Rejecting epoch based on EOG : ['EOG 062']
import mne
from mne import find_events, Epochs, pick_types, read_evokeds
from mne.datasets.megsim import load_data
print(__doc__)
condition = 'visual' # or 'auditory' or 'somatosensory'
# Load experimental RAW files for the visual condition
raw_fnames = load_data(condition=condition, data_format='raw',
data_type='experimental', verbose=True)
# Load simulation evoked files for the visual condition
evoked_fnames = load_data(condition=condition, data_format='evoked',
data_type='simulation', verbose=True)
raw = mne.io.read_raw_fif(raw_fnames[0], verbose='error') # Bad naming
events = find_events(raw, stim_channel="STI 014", shortest_event=1)
# Visualize raw file
raw.plot()
# Make an evoked file from the experimental data
picks = pick_types(raw.info, meg=True, eog=True, exclude='bads')
# Read epochs
event_id, tmin, tmax = 9, -0.2, 0.5
epochs = Epochs(raw, events, event_id, tmin, tmax, baseline=(None, 0),
picks=picks, reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average() # average epochs and get an Evoked dataset.
evoked.plot(time_unit='s')
# Compare to the simulated data (use verbose='error' b/c of naming)
evoked_sim = read_evokeds(evoked_fnames[0], condition=0, verbose='error',
baseline=(None, 0))
evoked_sim.plot(time_unit='s')
Total running time of the script: ( 0 minutes 15.685 seconds)
Estimated memory usage: 14 MB