def find_pattern():
global mneraw,icas,ica_pic_path,pattern_ica_id,pattern_subject_id
raw=mneraw[0]
ica=icas[0]
fig1,fig2=corrmap(icas,template=(pattern_subject_id,pattern_ica_id),threshold=ica_threshold,show=False,label="blink")
fig1.savefig(os.path.join(ica_pic_path,"blink_pattern.jpg"))
print(type(fig2))
if type(fig2)==list:
for j in range(len(fig2)):
fig2[j].savefig(os.path.join(ica_pic_path,"blink_found_{}.jpg".format(j+1)))
else:
fig2.savefig(os.path.join(ica_pic_path,"blink_found.jpg"))
def remove_eog_template_ica(raws, n_components, ch_name, threshold):
"""Remove EOG artifacts by similar Independent Components across subjects.
Parameters
----------
raws : list
List Raw instances.
n_components : int
Number of principal components for ICA.
ch_name : str
The name of the channel to use for EOG peak detection.
threshold : int
The value above which a feature is classified as outlier.
Returns
-------
raws : list
List Raw instances.
Notes
-----
"""
icas = [
ICA(n_components=n_components, max_iter='auto').copy().fit(raw,
verbose=0)
for raw in raws
]
for raw, ica in zip(raws, icas):
eog_inds, _ = ica.find_bads_eog(raw, ch_name=ch_name, verbose=0)
if eog_inds:
break
if not eog_inds:
raise RuntimeError(
'This group of subjects does not have an EOG template, add more subjects.'
)
_ = corrmap(icas,
template=(0, eog_inds[0]),
threshold=threshold,
label='blink')
for raw, ica in zip(raws, icas):
ica.exclude = ica.labels_['blink']
ica.apply(raw, verbose=0)
return raws
def ICA_apply(icas: int, subj_number: int, comp_number: int,
epochs: list) -> list:
"""
Applies ICA with template model from 1 participant in the dyad.
See ICA_choice_comp for a detailed description of the parameters and output.
"""
cleaned_epochs_ICA = []
# selecting which ICs corresponding to the template
template_eog_component = icas[subj_number].get_components()[:, comp_number]
# applying corrmap with at least 1 component detected for each subj
fig_template, fig_detected = corrmap(icas,
template=template_eog_component,
threshold=0.9,
label='blink',
ch_type='eeg')
# labeling the ICs that capture blink artifacts
print([ica.labels_ for ica in icas])
# selecting ICA components after viz
for i in icas:
i.exclude = i.labels_['blink']
epoch_all_ch = []
# applying ica on clean_epochs
# for each participant
for i, j in zip(range(0, len(epochs)), icas):
# taking all channels to apply ICA
bads = epochs[i].info['bads']
epoch_all_ch.append(mne.Epochs.copy(epochs[i]))
epoch_all_ch[i].info['bads'] = []
j.apply(epoch_all_ch[i])
epoch_all_ch[i].info['bads'] = bads
cleaned_epochs_ICA.append(epoch_all_ch[i])
return cleaned_epochs_ICA
raw.rename_channels(mapping)
raw.set_montage('standard_1005')
# fit ICA
ica = ICA(n_components=30, random_state=97)
ica.fit(raw)
raws.append(raw)
icas.append(ica)
###############################################################################
# Now let's run :func:`~mne.preprocessing.corrmap`:
# use the first subject as template; use Fpz as proxy for EOG
raw = raws[0]
ica = icas[0]
eog_inds, eog_scores = ica.find_bads_eog(raw, ch_name='Fpz')
corrmap(icas, template=(0, eog_inds[0]))
###############################################################################
# The first figure shows the template map, while the second figure shows all
# the maps that were considered a "match" for the template (including the
# template itself). There were only three matches from the four subjects;
# notice the output message ``No maps selected for subject(s) 1, consider a
# more liberal threshold``. By default the threshold is set automatically by
# trying several values; here it may have chosen a threshold that is too high.
# Let's take a look at the ICA sources for each subject:
for index, (ica, raw) in enumerate(zip(icas, raws)):
fig = ica.plot_sources(raw, show_scrollbars=False)
fig.subplots_adjust(top=0.9) # make space for title
fig.suptitle('Subject {}'.format(index))
def corr_map(list_icas,
ica_template,
comp_template,
dir_templates,
label,
threshold=0.85):
"""
apply a corr map modify objects list_icas inplace
:param list_icas: list of icas objects
:param ica_template: int positional int of the ica to use as template
:param comp_template: list of int, list of components
:param dir_templates: string, diretory to save templates
:param threshold: float64, set the threshold
:param label: string, label
"""
for comp in comp_template:
corr = corrmap(list_icas,
template=(ica_template, comp),
plot=True,
show=False,
threshold=threshold,
label=label)
path_topos = os.path.join(dir_templates, str(comp) + ".png")
corr[0].savefig(path_topos)
topos = Image.open(path_topos)
if type(corr[1]) == list:
lst = [topos]
paths = [path_topos]
total_width = 0
total_height = 0
for ind, im in enumerate(corr[1]):
path_im = os.path.join(dir_templates,
str(comp) + str(ind) + ".png")
paths.append(path_im)
im.savefig(path_im)
scalps = Image.open(path_im)
lst.append(scalps)
width, height = scalps.size
total_width += width
if height > total_height:
total_height = height
new_im = Image.new('RGBA', (total_width, total_height))
x_offset = 0
for scalp in lst:
new_im.paste(scalp, (x_offset, 0))
x_offset += scalp.size[0]
new_im.save(
os.path.join(dir_templates,
"component" + str(comp) + ".png"))
# for i in lst:
# i.close()
for p in paths:
os.remove(p)
plt.close('all')
else:
path_found = os.path.join(dir_templates,
str(comp) + "a" + ".png")
corr[1].savefig(path_found)
topos = Image.open(path_topos)
found = Image.open(path_found)
img = [topos, found]
width_0, height_0 = img[0].size
width_1, height_1 = img[1].size
real_width = width_0 + width_1
real_height = height_1
new_im = Image.new('RGBA', (real_width, real_height))
x_offset = 0
for im in img:
new_im.paste(im, (x_offset, 0))
x_offset += im.size[0]
new_im.save(
os.path.join(dir_templates,
"component" + str(comp) + ".png"))
os.remove(path_topos)
os.remove(path_found)
plt.close('all')
montage = make_standard_montage('standard_1005') #Caricare il montaggio
raw.set_montage(montage) #Setto il montaggio
raw.filter(1.0, 79.0, fir_design='firwin', skip_by_annotation='edge') #Filtro
raw_notch = raw.notch_filter(freqs=60) #Faccio un filtro passa banda
raw.plot_psd(area_mode=None, show=False, average=False, fmin =1.0, fmax=80.0, dB=False, n_fft=160)
# todo: qui salvare il plot psd
# Ica
ica = ICA(n_components=64, random_state=42, method="fastica", max_iter=1000)
ind = []
for index, value in enumerate((raw.annotations).description):
if value == "BAD boundary" or value == "EDGE boundary":
ind.append(index)
(raw.annotations).delete(ind)
ica.fit(raw)
raws.append(raw)
icas.append(ica)
#%%
icas[0].plot_properties(raws[0], picks = [2,3], dB = False)
eog_inds, eog_scores = icas[0].find_bads_eog(raws[0],ch_name='Fp1')
icas[0].plot_components(picks = eog_inds)
exc_0 = []
corr_map = corrmap(icas, template=(0, eog_inds[0]))
exc_66 = [0,1,3,13,14,17,29,32,33,35,47,50,53]
#Here we report what kind of artifact
eyes = [0,1,3,14,33,35,47]
#ecg = []
#movement =[17,32,]
#odd = [13]
#electrode =[53]
#%% Correlational map
#exc_template =
corrmap(icas, template=(0, 33), plot=True, threshold=0.75, label = 'artifact Subject' )
for subj in range (len(subjects)):
print(str(icas_load[subj].labels_ ))
artifacts_dic = []
#artifacts_dic.append(icas[subj].labels_.items())
#artifacts_dic.append(icas[subj].labels_)
print(artifacts_dic)
# print(' Subject' + str(subjects[subj]) +' ' + str([icas[subj].labels_ for ica in icas]))
i = icas_load[1].labels_
temp_icas.append(
read_ica(
fname.output(subject=subj,
processing_step='fit_ica',
file_type='ica.fif')))
# set thresholds for correlation
if subject in {5, 28, 32, 39, 45}:
threshold = 0.90
else:
threshold = 0.85
# compute correlations with template ocular movements up/down and left/right
corrmap(icas=[temp_icas[1], ica],
template=(0, 0),
threshold=threshold,
label='blink_up',
plot=False)
corrmap(icas=[temp_icas[1], ica],
template=(0, 1),
threshold=threshold,
label='blink_side',
plot=False)
# compute correlations with template ocular movements that look slightly
# different
corrmap(icas=[temp_icas[0], ica],
template=(0, 0),
threshold=threshold,
label='blink_misc',
plot=False)
for subj in temp_subjs:
temp_raws.append(
read_raw_fif(
fname.output(subject=subj,
processing_step='repair_bads',
file_type='raw.fif')))
temp_icas.append(
read_ica(
fname.output(subject=subj,
processing_step='fit_ica',
file_type='ica.fif')))
# compute correlations with template ocular movements up/down and left/right
corrmap(icas=[temp_icas[0], ica],
template=(0, 0),
threshold=0.85,
label='blink_up',
plot=False)
corrmap(icas=[temp_icas[0], ica],
template=(0, 7),
threshold=0.85,
label='blink_side',
plot=False)
###############################################################################
# 4) Create summary plots to show signal correction on main experimental
# condition
# create target epochs
target_evs = events_from_annotations(raw, regexp='(11)|(12)|(21)|(22)')[0]
target_epo = Epochs(raw,
ica = ICA(n_components=64,
random_state=42,
method="fastica",
max_iter=1000)
ind = []
for index, value in enumerate((raw.annotations).description):
if value == "BAD boundary" or value == "EDGE boundary":
ind.append(index)
(raw.annotations).delete(ind)
ica.fit(raw)
raws.append(raw)
icas.append(ica)
corrmap(icas, template=(0, 19), plot=True, threshold=0.80)
icas[0].plot_properties(
raws[0],
picks=[26, 29, 34, 44, 47, 49, 51, 53, 55, 56, 58, 59, 63, 60, 61],
dB=False)
#%%
#eog_inds, eog_scores = icas[0].find_bads_eog(raws[0], ch_name='Fpz')
#icas[0].plot_properties(raws[0], picks=[], dB=False)
exc_0 = [
0, 3, 9, 11, 14, 15, 19, 26, 29, 34, 44, 47, 49, 51, 53, 55, 56, 58, 59,
63, 60, 61
]
maybe = [7, 12, 23, 24, 40]
#reconst_raw = raws[0].copy()
#icas[0].plot_overlay(reconst_raw, exclude=exc_0)
