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
"""
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])
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
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
"""
from mne import read_evokeds, combine_evoked
from mne.datasets.megsim import load_data
print(__doc__)
condition = 'visual' # or 'auditory' or 'somatosensory'
# Load experimental RAW files for the visual condition
epochs_fnames = load_data(condition=condition, data_format='single-trial',
data_type='simulation', verbose=True)
# Take only 10 trials from the same simulation setup.
epochs_fnames = [f for f in epochs_fnames if 'sim6_trial_' in f][:10]
evokeds = [read_evokeds(f)[0] for f in epochs_fnames]
mean_evoked = combine_evoked(evokeds, weights='nave')
# Visualize the average
mean_evoked.plot()
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
"""
import pylab as pl
import mne
from mne.datasets.megsim import load_data
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')
# Load simulation evoked files for the visual condition
evoked_fnames = load_data(condition=condition,
data_format='evoked',
data_type='simulation')
raw = mne.fiff.Raw(raw_fnames[0])
events = mne.find_events(raw, stim_channel="STI 014")
# Visualize raw file
raw.plot()
# Make an evoked file from the experimental data
picks = mne.fiff.pick_types(raw.info, meg=True, eog=True, exclude='bads')
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
"""
from mne import read_evokeds, combine_evoked
from mne.datasets.megsim import load_data
print(__doc__)
condition = 'visual' # or 'auditory' or 'somatosensory'
# Load experimental RAW files for the visual condition
epochs_fnames = load_data(condition=condition,
data_format='single-trial',
data_type='simulation',
verbose=True)
# Take only 10 trials from the same simulation setup.
epochs_fnames = [f for f in epochs_fnames if 'sim6_trial_' in f][:10]
evokeds = [read_evokeds(f, verbose='error')[0] for f in epochs_fnames]
mean_evoked = combine_evoked(evokeds, weights='nave')
# Visualize the average
mean_evoked.plot()
====================================== 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 """ import mne from mne.datasets.megsim import load_data condition = "visual" # or 'auditory' or 'somatosensory' # Load experimental RAW files for the visual condition epochs_fnames = load_data(condition=condition, data_format="single-trial", data_type="simulation") # Take only 10 trials from the same simulation setup. epochs_fnames = [f for f in epochs_fnames if "sim6_trial_" in f][:10] evokeds = [mne.fiff.read_evoked(f) for f in epochs_fnames] mean_evoked = sum(evokeds[1:], evokeds[0]) # Visualize the average mean_evoked.plot()