/
lemon_support.py
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/
lemon_support.py
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import os
import osl
import numpy as np
import mne
import sails
from scipy import io, ndimage, stats
import matplotlib.pyplot as plt
from glm_config import cfg
import logging
logger = logging.getLogger('osl')
def lemon_set_standard_montage(glmsp):
from pathlib import Path
pth = Path(mne.channels.__file__).parent
pth = pth / 'data' / 'montages' / 'brainproducts-RNP-BA-128.txt'
fbase = os.path.join(cfg['lemon_processed_data'], 'sub-010002', 'sub-010002_preproc_raw.fif')
reference = mne.io.read_raw_fif(fbase).pick_types(eeg=True)
new = []
with open(pth, 'r') as f:
for line in f.readlines():
tag = line.split(' ')[0]
if tag == 'Name' or tag in reference.ch_names:
new.append(line)
new_mon = os.path.join(cfg['code_dir'], 'lemon_custom_montage.txt')
with open(new_mon, 'w') as f:
for line in new:
f.write(line)
from mne.channels._standard_montage_utils import _read_theta_phi_in_degrees
mon = _read_theta_phi_in_degrees(new_mon, mne.defaults.HEAD_SIZE_DEFAULT)
reference = reference.set_montage(mon)
glmsp.info = reference.info
return glmsp
def lemon_set_channel_montage(dataset, userargs):
logger.info('LEMON Stage - load and set channel montage')
logger.info('userargs: {0}'.format(str(userargs)))
subj = '010060'
#base = f'/Users/andrew/Projects/lemon/EEG_Raw_BIDS_ID/sub-{subj}/RSEEG/'
#base = f'/ohba/pi/knobre/datasets/MBB-LEMON/EEG_MPILMBB_LEMON/EEG_Raw_BIDS_ID/sub-{subj}/RSEEG/'
ref_file = os.path.join(cfg['lemon_raw'], f'sub-{subj}', 'RSEEG', f'sub-{subj}.mat')
ref_file = os.path.join(cfg['code_dir'], 'sub-010060.mat')
X = io.loadmat(ref_file)
ch_pos = {}
for ii in range(len(X['Channel'][0])-1): #final channel is reference
key = X['Channel'][0][ii][0][0].split('_')[2]
if key[:2] == 'FP':
key = 'Fp' + key[2]
value = X['Channel'][0][ii][3][:, 0]
value = np.array([value[1], value[0], value[2]])
ch_pos[key] = value
dig = mne.channels.make_dig_montage(ch_pos=ch_pos)
dataset['raw'].set_montage(dig)
return dataset
def get_eeg_data(raw, csd=True):
"""Load EEG and perform sanity checks."""
# Use first scan as reference for channel labels and order
fbase = os.path.join(cfg['lemon_processed_data'], 'sub-010002', 'sub-010002_preproc_raw.fif')
reference = mne.io.read_raw_fif(fbase).pick_types(eeg=True)
mon = reference.get_montage()
# Load ideal layout and match data-channels
raw = raw.copy().pick_types(eeg=True)
ideal_inds = [mon.ch_names.index(c) for c in raw.info['ch_names']]
if csd:
# Apply laplacian if requested
raw = mne.preprocessing.compute_current_source_density(raw)
X = raw.get_data(picks='csd')
else:
# Get data from EEG picks
X = raw.get_data(picks='eeg')
# Preallocate & store ouput
Y = np.zeros((len(mon.ch_names), X.shape[1]))
Y[ideal_inds, :] = X
return Y
def lemon_create_heog(dataset, userargs):
logger.info('LEMON Stage - Create HEOG from F7 and F8')
logger.info('userargs: {0}'.format(str(userargs)))
F7 = dataset['raw'].get_data(picks='F7')
F8 = dataset['raw'].get_data(picks='F8')
heog = F7-F8
info = mne.create_info(['HEOG'],
dataset['raw'].info['sfreq'],
['eog'])
eog_raw = mne.io.RawArray(heog, info)
dataset['raw'].add_channels([eog_raw], force_update_info=True)
return dataset
def lemon_ica(dataset, userargs, logfile=None):
logger.info('LEMON Stage - custom EEG ICA function')
logger.info('userargs: {0}'.format(str(userargs)))
# NOTE: **userargs doesn't work because 'picks' is in there
ica = mne.preprocessing.ICA(n_components=userargs['n_components'],
max_iter=1000,
random_state=42)
# https://mne.tools/stable/auto_tutorials/preprocessing/40_artifact_correction_ica.html#filtering-to-remove-slow-drifts
fraw = dataset['raw'].copy().filter(l_freq=1., h_freq=None)
ica.fit(fraw, picks=userargs['picks'])
dataset['ica'] = ica
logger.info('starting EOG autoreject')
# Find and exclude VEOG
#eog_indices, eog_scores = dataset['ica'].find_bads_eog(dataset['raw'])
veog_indices, eog_scores = dataset['ica'].find_bads_eog(dataset['raw'], 'VEOG')
if len(veog_indices) == 0:
veog_indices, eog_scores = dataset['ica'].find_bads_eog(dataset['raw'], 'VEOG', threshold=2)
dataset['veog_scores'] = eog_scores
dataset['ica'].exclude.extend(veog_indices)
logger.info('Marking {0} ICs as EOG {1}'.format(len(dataset['ica'].exclude),
veog_indices))
# Find and exclude HEOG
#heog_indices = lemon_find_heog(fraw, ica)
heog_indices, eog_scores = dataset['ica'].find_bads_eog(dataset['raw'], 'HEOG')
if len(heog_indices) == 0:
heog_indices, eog_scores = dataset['ica'].find_bads_eog(dataset['raw'], 'HEOG', threshold=2)
dataset['heog_scores'] = eog_scores
dataset['ica'].exclude.extend(heog_indices)
logger.info('Marking {0} ICs as HEOG {1}'.format(len(heog_indices),
heog_indices))
# Save components as channels in raw object
src = dataset['ica'].get_sources(fraw).get_data()
veog = src[veog_indices[0], :]
heog = src[heog_indices[0], :]
ica.labels_['top'] = [veog_indices[0], heog_indices[0]]
info = mne.create_info(['ICA-VEOG', 'ICA-HEOG'],
dataset['raw'].info['sfreq'],
['misc', 'misc'])
eog_raw = mne.io.RawArray(np.c_[veog, heog].T, info)
dataset['raw'].add_channels([eog_raw], force_update_info=True)
# Apply ICA denoising or not
if ('apply' not in userargs) or (userargs['apply'] is True):
logger.info('Removing selected components from raw data')
dataset['ica'].apply(dataset['raw'])
else:
logger.info('Components were not removed from raw data')
return dataset
def lemon_zapline_dss(dataset, userargs, logfile=None):
logger.info('LEMON Stage - ZapLine power removal')
logger.info('userargs: {0}'.format(str(userargs)))
from meegkit import dss
# https://mne.discourse.group/t/clean-line-noise-zapline-method-function-for-mne-using-meegkit-toolbox/7407
fline = userargs.get('fline', 50)
data = dataset['raw'].get_data() # Convert mne data to numpy darray
sfreq = dataset['raw'].info['sfreq'] # Extract the sampling freq
#Apply MEEGkit toolbox function
out, _ = dss.dss_line(data.T, fline, sfreq, nremove=4) # fline (Line noise freq) = 50 Hz for Europe
dataset['raw']._data = out.T # Overwrite old data
return dataset
def lemon_make_task_regressor(dataset):
ev, ev_id = mne.events_from_annotations(dataset['raw'])
print('Found {0} events in raw'.format(ev.shape[0]))
print(ev_id)
# Correct for cropping first 10 seconds - not sure why this is necessary?!
ev[:, 0] -= dataset['raw'].first_samp
task = np.zeros((dataset['raw'].n_times,))
for ii in range(ev.shape[0]):
if ev[ii, 2] == ev_id['Stimulus/S200']:
# EYES OPEN
task[ev[ii,0]:ev[ii,0]+5000] = 1
elif ev[ii, 2] == ev_id['Stimulus/S210']:
# EYES CLOSED
task[ev[ii,0]:ev[ii,0]+5000] = -1
elif ev[ii, 2] == 1:
task[ev[ii,0]] = task[ev[ii,0]-1]
return task
def find_eog_events(raw, event_id=998):
eog = raw.copy().filter(l_freq=1, h_freq=10, picks='eog').get_data(picks='VEOG')
eog = eog[0, :]
# 10 seconds hopefully long enough to avoid rejecting real blinks - only
# want to catch HUGE artefacts here.
bads = sails.utils.detect_artefacts(eog, axis=0, reject_mode='segments', segment_len=2500)
eog[bads] = np.median(eog)
logger.info('Removed {0} bad samples from EOG ({1}%)'.format(bads.sum(), 100*(bads.sum()/len(bads))))
if np.abs(np.max(eog)) > np.abs(np.min(eog)):
eog_events, _ = mne.preprocessing.eog.peak_finder(eog,
None, extrema=1)
else:
eog_events, _ = mne.preprocessing.eog.peak_finder(eog,
None, extrema=-1)
n_events = len(eog_events)
logger.info(f'Number of EOG events detected: {n_events}')
#eog_events = np.array([eog_events + raw.first_samp,
eog_events = np.array([eog_events,
np.zeros(n_events, int),
event_id * np.ones(n_events, int)]).T
return eog_events
def lemon_make_blinks_regressor(raw, corr_thresh=0.75, figpath=None):
#eog_events = mne.preprocessing.find_eog_events(raw, l_freq=1, h_freq=10)
eog_events = find_eog_events(raw)
logger.info('found {0} blinks'.format(eog_events.shape[0]))
#eog_events = find_eog_events(raw)
#logger.info('found {0} blinks'.format(eog_events.shape[0]))
# Correct for cropping first 10 seconds - not sure why this is necessary?!
#eog_events[:, 0] -= int(10*raw.info['sfreq'])
tmin = -0.1
tmax = 0.15
epochs = mne.Epochs(raw, eog_events, 998, tmin, tmax, picks='eog')
ev_eog = epochs.get_data()[:, 0, :]
C = np.abs(np.corrcoef(ev_eog.mean(axis=0), ev_eog)[1:,0])
drops = np.where(C < corr_thresh)[0]
clean = epochs.copy().drop(drops)
keeps = np.where(C > corr_thresh)[0]
dirty = epochs.copy().drop(keeps)
eog_events = np.delete(eog_events, drops, axis=0)
logger.info('found {0} clean blinks'.format(eog_events.shape[0]))
blink_covariate = np.zeros((raw.n_times,))
#blink_covariate[eog_events[:, 0] - raw.first_samp] = 1
blink_covariate[eog_events[:, 0]] = 1
blink_covariate = ndimage.maximum_filter(blink_covariate,
size=raw.info['sfreq']//2)
if figpath is not None:
plt.figure(figsize=(16, 10))
plt.subplot(231)
plt.plot(epochs.times, epochs.get_data()[:, 0, :].mean(axis=0))
plt.title('All blinks')
plt.subplot(234)
plt.plot(epochs.times, epochs.get_data()[:, 0, :].T)
plt.subplot(232)
plt.plot(epochs.times, clean.get_data()[:, 0, :].mean(axis=0))
plt.title('Clean blinks')
plt.subplot(235)
plt.plot(epochs.times, clean.get_data()[:, 0, :].T)
plt.subplot(233)
plt.title('Dirty blinks')
plt.plot(epochs.times, dirty.get_data()[:, 0, :].mean(axis=0))
plt.subplot(236)
plt.plot(epochs.times, dirty.get_data()[:, 0, :].T)
plt.savefig(figpath, transparent=False, dpi=300)
return blink_covariate, eog_events.shape[0], clean.average(picks='eog')
def lemon_make_bads_regressor(raw, mode='eeg'):
bads = np.zeros((raw.n_times,))
for an in raw.annotations:
if an['description'].startswith('bad') and an['description'].endswith(mode):
start = raw.time_as_index(an['onset'])[0] - raw.first_samp
duration = int(an['duration'] * raw.info['sfreq'])
bads[start:start+duration] = 1
if mode == 'raw':
bads[:int(raw.info['sfreq']*2)] = 1
bads[-int(raw.info['sfreq']*2):] = 1
else:
bads[:int(raw.info['sfreq'])] = 1
bads[-int(raw.info['sfreq']):] = 1
return bads
def quick_plot_eog_icas(raw, ica, figpath=None):
inds = np.arange(250*45, 250*300)
plt.figure(figsize=(16, 9))
veog = raw.get_data(picks='VEOG')[0, :]
ica_veog = raw.get_data(picks='ICA-VEOG')[0, :]
ax = plt.axes([0.05, 0.55, 0.125, 0.4])
comp = ica.get_components()[:, ica.labels_['top'][0]]
mne.viz.plot_topomap(comp, ica.info, axes=ax, show=False)
plt.axes([0.2, 0.55, 0.475, 0.4])
plt.plot(stats.zscore(veog[inds]))
plt.plot(stats.zscore(ica_veog[inds])-10)
plt.legend(['VEOGs', 'ICA-VEOG'], frameon=False)
plt.xlim(0, 250*180)
plt.axes([0.725, 0.55, 0.25, 0.4])
plt.plot(veog, ica_veog, '.k')
veog = raw.get_data(picks='VEOG', reject_by_annotation='omit')[0, :]
ica_veog = raw.get_data(picks='ICA-VEOG', reject_by_annotation='omit')[0, :]
plt.plot(veog, ica_veog, '.r')
plt.xlabel('VEOG'); plt.ylabel('ICA-VEOG')
plt.plot(veog, ica_veog, '.r')
plt.legend(['Samples', 'Clean Samples'], frameon=False)
plt.title('Correlation : r = {0}'.format(np.corrcoef(veog, ica_veog)[0, 1]))
heog = raw.get_data(picks='HEOG')[0, :]
ica_heog = raw.get_data(picks='ICA-HEOG')[0, :]
plt.axes([0.05, 0.05, 0.125, 0.4])
comp = ica.get_components()[:, ica.labels_['top'][1]]
mne.viz.plot_topomap(comp, ica.info, show=False)
plt.axes([0.2, 0.05, 0.475, 0.4])
plt.plot(stats.zscore(heog[inds]))
plt.plot(stats.zscore(ica_heog[inds])-5)
plt.legend(['HEOGs', 'ICA-HEOG'], frameon=False)
plt.xlim(0, 250*180)
plt.axes([0.725, 0.05, 0.25, 0.4])
plt.plot(heog, ica_heog, '.k')
heog = raw.get_data(picks='HEOG', reject_by_annotation='omit')[0, :]
ica_heog = raw.get_data(picks='ICA-HEOG', reject_by_annotation='omit')[0, :]
plt.plot(heog, ica_heog, '.r')
plt.legend(['Samples', 'Clean Samples'], frameon=False)
plt.xlabel('HEOG'); plt.ylabel('ICA-HEOG')
plt.title('Correlation : r = {0}'.format(np.corrcoef(heog, ica_heog)[0, 1]))
plt.savefig(figpath, transparent=False, dpi=300)
def quick_plot_eog_epochs(raw, figpath=None):
fig = mne.preprocessing.create_eog_epochs(raw, picks='eeg').average().plot_joint(show=False)
fig.savefig(figpath.format('eeg_eog_epochs'))
fig = mne.preprocessing.create_eog_epochs(raw).average().plot(show=False)
fig.savefig(figpath.format('eog_eog_epochs'))
def plot_design(ax, design_matrix, regressor_names):
num_observations, num_regressors = design_matrix.shape
vm = np.max((design_matrix.min(), design_matrix.max()))
cax = ax.pcolor(design_matrix, cmap=plt.cm.coolwarm,
vmin=-vm, vmax=vm)
ax.set_xlabel('Regressors')
tks = np.arange(len(regressor_names)+1)
ax.set_xticks(tks+0.5)
ax.set_xticklabels(tks)
tkstep = 2
tks = np.arange(0, design_matrix.shape[0], tkstep)
for tag in ['top', 'right', 'left', 'bottom']:
ax.spines[tag].set_visible(False)
summary_lines = True
new_cols = 0
for ii in range(num_regressors):
if summary_lines:
x = design_matrix[:, ii]
if np.abs(np.diff(x)).sum() != 0:
y = (0.5*x) / (np.max(np.abs(x)) * 1.1)
else:
# Constant regressor
y = np.ones_like(x) * .45
if num_observations > 50:
ax.plot(y+ii+new_cols+0.5, np.arange(0, 0+num_observations)+.5, 'k')
else:
yy = y+ii+new_cols+0.5
print('{} - {} - {}'.format(yy.min(), yy.mean(), yy.max()))
ax.plot(y+ii+new_cols+0.5, np.arange(0, 0+num_observations)+.5,
'k|', markersize=5)
# Add white dividing line
if ii < num_regressors-1:
ax.plot([ii+1+new_cols, ii+1+new_cols], [0, 0+num_observations],
'w', linewidth=4)
return cax