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%matplotlib inline

MEGSIM experimental and simulation datasets

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



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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')