def test_1020_selection():
"""Test making a 10/20 selection dict."""
base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname = op.join(base_dir, 'test_raw.set')
loc_fname = op.join(base_dir, 'test_chans.locs')
raw = read_raw_eeglab(raw_fname, preload=True)
montage = read_custom_montage(loc_fname)
raw = raw.rename_channels(dict(zip(raw.ch_names, montage.ch_names)))
raw.set_montage(montage)
for input in ("a_string", 100, raw, [1, 2]):
pytest.raises(TypeError, make_1020_channel_selections, input)
sels = make_1020_channel_selections(raw.info)
# are all frontal channels placed before all occipital channels?
for name, picks in sels.items():
fs = min([
ii for ii, pick in enumerate(picks)
if raw.ch_names[pick].startswith("F")
])
ps = max([
ii for ii, pick in enumerate(picks)
if raw.ch_names[pick].startswith("O")
])
assert fs > ps
# are channels in the correct selection?
fz_c3_c4 = [raw.ch_names.index(ch) for ch in ("Fz", "C3", "C4")]
for channel, roi in zip(fz_c3_c4, ("Midline", "Left", "Right")):
assert channel in sels[roi]
def permutation_cluster_analysis(epochs, n_permutations=1000, plot=True):
"""
Do a spatio-temporal cluster analyis to compare experimental conditions.
"""
# get the data for each event in epochs.evet_id transpose because the cluster test requires
# channels to be last. In this case, inference is done over items. In the same manner, we could
# also conduct the test over, e.g., subjects.
tfce = dict(start=.2, step=.2)
time_unit = dict(time_unit="s")
events = list(epochs.event_id.keys())
if plot:
if len(events) == 2: # When comparing two events subtract evokeds
evoked = combine_evoked([epochs[events[0]].average(), -epochs[events[1]].average()],
weights='equal')
title = "%s vs %s" % (events[0], events[1])
elif len(events) > 2: # When comparing more than two events verage them
evoked = combine_evoked([epochs[e].average() for e in events], weights='equal')
evoked.data /= len(events)
title = ""
for e in events:
title += e+" + "
title = title[:-2]
evoked.plot_joint(title=title, ts_args=time_unit, topomap_args=time_unit)
X = [epochs[e].get_data().transpose(0, 2, 1) for e in events]
t_obs, clusters, cluster_pv, h0 = spatio_temporal_cluster_test(X, tfce, n_permutations)
significant_points = cluster_pv.reshape(t_obs.shape).T < .05
selections = make_1020_channel_selections(evoked.info, midline="12z")
fig, axes = plt.subplots(nrows=3, figsize=(8, 8))
axes = {sel: ax for sel, ax in zip(selections, axes.ravel())}
evoked.plot_image(axes=axes, group_by=selections, colorbar=False, show=False,
mask=significant_points, show_names="all", titles=None,
**time_unit)
plt.colorbar(axes["Left"].images[-1], ax=list(axes.values()), shrink=.3, label="µV")
plt.show()
def test_1020_selection():
"""Test making a 10/20 selection dict."""
base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
raw_fname = op.join(base_dir, 'test_raw.set')
loc_fname = op.join(base_dir, 'test_chans.locs')
raw = read_raw_eeglab(raw_fname, montage=loc_fname)
for input in ("a_string", 100, raw, [1, 2]):
pytest.raises(TypeError, make_1020_channel_selections, input)
sels = make_1020_channel_selections(raw.info)
# are all frontal channels placed before all occipital channels?
for name, picks in sels.items():
fs = min([ii for ii, pick in enumerate(picks)
if raw.ch_names[pick].startswith("F")])
ps = max([ii for ii, pick in enumerate(picks)
if raw.ch_names[pick].startswith("O")])
assert fs > ps
# are channels in the correct selection?
fz_c3_c4 = [raw.ch_names.index(ch) for ch in ("Fz", "C3", "C4")]
for channel, roi in zip(fz_c3_c4, ("Midline", "Left", "Right")):
assert channel in sels[roi]
# :class:`mne.Evoked` objects as images (via :class:`mne.Evoked.plot_image`)
# and masking points for significance.
# Here, we group channels by Regions of Interest to facilitate localising
# effects on the head.
# We need an evoked object to plot the image to be masked
evoked = mne.combine_evoked(
[long_words.average(), short_words.average()],
weights=[1, -1]) # calculate difference wave
time_unit = dict(time_unit="s")
evoked.plot_joint(title="Long vs. short words",
ts_args=time_unit,
topomap_args=time_unit) # show difference wave
# Create ROIs by checking channel labels
selections = make_1020_channel_selections(evoked.info, midline="12z")
# Visualize the results
fig, axes = plt.subplots(nrows=3, figsize=(8, 8))
axes = {sel: ax for sel, ax in zip(selections, axes.ravel())}
evoked.plot_image(axes=axes,
group_by=selections,
colorbar=False,
show=False,
mask=significant_points,
show_names="all",
titles=None,
**time_unit)
plt.colorbar(axes["Left"].images[-1],
ax=list(axes.values()),
shrink=.3,
tmin=0.,
tmax=tmax,
new_id=2)
epochs = mne.Epochs(raw,
events=new_events,
tmax=tmax + .1,
event_id={"square": 2})
###############################################################################
# Plot using GFP
# Parameters for plotting
order = rts.argsort() # sorting from fast to slow trials
selections = make_1020_channel_selections(epochs.info, midline="12z")
# The actual plots (GFP)
epochs.plot_image(group_by=selections,
order=order,
sigma=1.5,
overlay_times=rts / 1000.,
combine='gfp',
ts_args=dict(vlines=[0, rts.mean() / 1000.]))
###############################################################################
# Plot using median
epochs.plot_image(group_by=selections,
order=order,
sigma=1.5,
file_type='epo.fif')
cue_epo = read_epochs(input_file, preload=True)
cue_epo = cue_epo['Correct A', 'Correct B'].copy()
cue_epo_nb = cue_epo.copy().crop(tmin=-0.250, tmax=2.450, include_tmax=False)
cue_epo = cue_epo.apply_baseline(baseline).crop(tmin=-0.300)
# save the generic info structure of cue epochs (i.e., channel names, number of
# channels, etc.).
epochs_info = cue_epo_nb.info
n_channels = len(epochs_info['ch_names'])
n_times = len(cue_epo_nb.times)
times = cue_epo_nb.times
tmin = cue_epo_nb.tmin
# split channels into ROIs for results section
selections = make_1020_channel_selections(epochs_info, midline='12z')
# placeholder for results
betas_evoked = dict()
r2_evoked = dict()
# ###############################################################################
# 2) loop through subjects and extract betas
for n_subj, subj in enumerate(subjects):
subj_beta = betas[n_subj, :]
subj_beta = subj_beta.reshape((n_channels, n_times))
betas_evoked[str(subj)] = EvokedArray(subj_beta, epochs_info, tmin)
subj_r2 = r2[n_subj, :]
subj_r2 = subj_r2.reshape((n_channels, n_times))
r2_evoked[str(subj)] = EvokedArray(subj_r2, epochs_info, tmin)
significant_points = cluster_pv.reshape(t_obs.shape).T < .05
print(str(significant_points.sum()) + " points selected by TFCE ...")
##############################################################################
# The results of these mass univariate analyses can be visualised by plotting
# :class:`mne.Evoked` objects as images (via :class:`mne.Evoked.plot_image`)
# and masking points for significance.
# Here, we group channels by Regions of Interest to facilitate localising
# effects on the head.
# We need an evoked object to plot the image to be masked
evoked = mne.combine_evoked([long_words.average(), -short_words.average()],
weights='equal') # calculate difference wave
time_unit = dict(time_unit="s")
evoked.plot_joint(title="Long vs. short words", ts_args=time_unit,
topomap_args=time_unit) # show difference wave
# Create ROIs by checking channel labels
selections = make_1020_channel_selections(evoked.info, midline="12z")
# Visualize the results
fig, axes = plt.subplots(nrows=3, figsize=(8, 8))
axes = {sel: ax for sel, ax in zip(selections, axes.ravel())}
evoked.plot_image(axes=axes, group_by=selections, colorbar=False, show=False,
mask=significant_points, show_names="all", titles=None,
**time_unit)
plt.colorbar(axes["Left"].images[-1], ax=list(axes.values()), shrink=.3,
label="uV")
plt.show()
events = mne.find_events(raw)
###############################################################################
# Create Epochs
# define target events:
# 1. find response times: distance between "square" and "rt" events
# 2. extract A. "square" events B. followed by a button press within 700 msec
tmax = .7
sfreq = raw.info["sfreq"]
reference_id, target_id = 2, 1
new_events, rts = define_target_events(events, reference_id, target_id, sfreq,
tmin=0., tmax=tmax, new_id=2)
epochs = Epochs(raw, events=new_events, tmax=tmax + .1,
event_id={"square": 2}, picks=picks)
###############################################################################
# Plot
# Parameters for plotting
order = rts.argsort() # sorting from fast to slow trials
selections = make_1020_channel_selections(epochs.info, midline="12z")
# The actual plots
for combine_measures in ('gfp', 'median'):
epochs.plot_image(group_by=selections, order=order, sigma=1.5,
overlay_times=rts / 1000., combine=combine_measures,
ts_args=dict(vlines=[0, rts.mean() / 1000.]))
fig.axes[0].spines['left'].set_bounds(-8, 8)
fig.axes[0].spines['bottom'].set_bounds(-.25, 2.5)
fig.axes[0].xaxis.set_label_coords(0.5, -0.2)
w, h = fig.get_size_inches()
fig.set_size_inches(w * 1.15, h * 1.15)
fig_name = fname.figures + '/Evoked_%s.pdf' % evoked.replace(' ', '_')
fig.savefig(fig_name, dpi=300)
###############################################################################
# 6) plot difference wave (Cue B - Cue A)
# compute difference wave
ab_diff = combine_evoked([ga_b_cue, -ga_a_cue], weights='equal')
# make channel ROIs for easier interpretation of the plot
selections = make_1020_channel_selections(ga_a_cue.info, midline='12z')
# get colormap and create figure
colormap = cm.get_cmap('RdBu_r')
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(23, 5.5))
for s, selection in enumerate(selections):
picks = selections[selection]
mask = abs(ab_diff.data) > 1.1e-6
ab_diff.plot_image(
xlim=[-0.25, 2.5],
picks=picks,
clim=dict(eeg=[-4, 4]),
colorbar=False,
axes=ax[s],
