meg=True,
eeg=False,
eog=True,
ecg=True,
stim=False,
exclude=raw.info['bads'])
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# Instead of the actual number of components here we pass a float value
# between 0 and 1 to select n_components by a percentage of
# explained variance.
ica = ICA(n_components=0.90,
max_n_components=100,
noise_cov=None,
random_state=0)
print ica
# get epochs
tmin, tmax, event_id = -0.2, 0.5, 1
# baseline = None
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
raw = Raw(raw_fname, preload=True)
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude=raw.info['bads'])
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# Sign and order of components is non deterministic.
# setting the random state to 0 makes the solution reproducible.
# Instead of the actual number of components we pass a float value
# between 0 and 1 to select n_components by a percentage of
# explained variance.
ica = ICA(n_components=0.90, max_n_components=100, noise_cov=None,
random_state=0)
print ica
# 1 minute exposure should be sufficient for artifact detection.
# However, rejection performance may significantly improve when using
# the entire data range
start, stop = raw.time_as_index([100, 160])
# decompose sources for raw data
ica.decompose_raw(raw, start=start, stop=stop, picks=picks)
print ica
sources = ica.get_sources_raw(raw, start=start, stop=stop)
# setup reasonable time window for inspection
start_plot, stop_plot = raw.time_as_index([100, 103])
eeg=False,
eog=False,
stim=False,
exclude=raw.info['bads'])
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# Sign and order of components is non deterministic.
# setting the random state to 0 makes the solution reproducible.
# Instead of the actual number of components we pass a float value
# between 0 and 1 to select n_components by a percentage of
# explained variance.
ica = ICA(n_components=0.90,
max_n_components=100,
noise_cov=None,
random_state=0)
# For maximum rejection performance we will compute the decomposition on
# the entire time range
# decompose sources for raw data, select n_components by explained variance
ica.decompose_raw(raw, start=None, stop=None, picks=picks)
print ica
# setup reasonable time window for inspection
start_plot, stop_plot = raw.time_as_index([100, 103])
# plot components
ica.plot_sources_raw(raw, start=start_plot, stop=stop_plot)
data_path = sample.data_path('..')
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = Raw(raw_fname, preload=True)
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=True,
ecg=True, stim=False, exclude=raw.info['bads'])
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# Instead of the actual number of components here we pass a float value
# between 0 and 1 to select n_components by a percentage of
# explained variance.
ica = ICA(n_components=0.90, max_n_components=100, noise_cov=None,
random_state=0)
print ica
# get epochs
tmin, tmax, event_id = -0.2, 0.5, 1
# baseline = None
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=baseline, preload=True, reject=reject)
# fit sources from epochs or from raw (both works for epochs)
ica.decompose_epochs(epochs)
raw = Raw(raw_fname, preload=True)
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude=raw.info['bads'])
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# Sign and order of components is non deterministic.
# setting the random state to 0 makes the solution reproducible.
# Instead of the actual number of components we pass a float value
# between 0 and 1 to select n_components by a percentage of
# explained variance.
ica = ICA(n_components=0.90, max_n_components=100, noise_cov=None,
random_state=0)
# For maximum rejection performance we will compute the decomposition on
# the entire time range
# decompose sources for raw data, select n_components by explained variance
ica.decompose_raw(raw, start=None, stop=None, picks=picks)
print ica
sources = ica.get_sources_raw(raw)
# setup reasonable time window for inspection
start_plot, stop_plot = raw.time_as_index([100, 103])
# plot components
ica.plot_sources_raw(raw, start=start_plot, stop=stop_plot)
