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Signal Enhancement Techniques#
In this document we investigate the effect of different signal enhancement techniques on functional near-infrared spectroscopy (fNIRS) data.
# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)
import os
import matplotlib.pyplot as plt
import mne
import mne_nirs
from mne_nirs.channels import picks_pair_to_idx
Import and preprocess data#
This code is exactly the same as the first sections in the MNE tutorial. See https://mne.tools/dev/auto_tutorials/preprocessing/plot_70_fnirs_processing.html for more details.
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir, verbose=True).load_data()
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
raw_haemo = mne_nirs.channels.get_long_channels(raw_haemo)
raw_haemo = raw_haemo.filter(0.05, 0.7, h_trans_bandwidth=0.2,
l_trans_bandwidth=0.02)
events, _ = mne.events_from_annotations(raw_haemo, event_id={'1.0': 1,
'2.0': 2,
'3.0': 3})
event_dict = {'Control': 1, 'Tapping/Left': 2, 'Tapping/Right': 3}
Loading /home/circleci/mne_data/MNE-fNIRS-motor-data/Participant-1
Extract epochs with no additional processing#
First we extract the epochs with no additional processing, this result should be the same as the MNE tutorial.
reject_criteria = dict(hbo=100e-6)
tmin, tmax = -5, 15
epochs = mne.Epochs(raw_haemo, events, event_id=event_dict,
tmin=tmin, tmax=tmax,
reject=reject_criteria, reject_by_annotation=True,
proj=True, baseline=(None, 0), preload=True,
detrend=None, verbose=True)
evoked_dict = {'Tapping/HbO': epochs['Tapping'].average(picks='hbo'),
'Tapping/HbR': epochs['Tapping'].average(picks='hbr'),
'Control/HbO': epochs['Control'].average(picks='hbo'),
'Control/HbR': epochs['Control'].average(picks='hbr')}
# Rename channels until the encoding of frequency in ch_name is fixed
for condition in evoked_dict:
evoked_dict[condition].rename_channels(lambda x: x[:-4])
Not setting metadata
90 matching events found
Setting baseline interval to [-4.992, 0.0] s
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 90 events and 157 original time points ...
Rejecting epoch based on HBO : ['S4_D4 hbo']
Rejecting epoch based on HBO : ['S4_D4 hbo']
Rejecting epoch based on HBO : ['S4_D4 hbo', 'S8_D8 hbo']
Rejecting epoch based on HBO : ['S8_D8 hbo']
Rejecting epoch based on HBO : ['S4_D4 hbo']
5 bad epochs dropped
Apply negative correlation enhancement algorithm#
Apply Cui et. al. 2010 and extract epochs.
raw_anti = mne_nirs.signal_enhancement.enhance_negative_correlation(raw_haemo)
epochs_anti = mne.Epochs(raw_anti, events, event_id=event_dict,
tmin=tmin, tmax=tmax,
reject=reject_criteria, reject_by_annotation=True,
proj=True, baseline=(None, 0), preload=True,
detrend=None, verbose=True)
evoked_dict_anti = {'Tapping/HbO': epochs_anti['Tapping'].average(picks='hbo'),
'Tapping/HbR': epochs_anti['Tapping'].average(picks='hbr'),
'Control/HbO': epochs_anti['Control'].average(picks='hbo'),
'Control/HbR': epochs_anti['Control'].average(picks='hbr')}
# Rename channels until the encoding of frequency in ch_name is fixed
for condition in evoked_dict_anti:
evoked_dict_anti[condition].rename_channels(lambda x: x[:-4])
Not setting metadata
90 matching events found
Setting baseline interval to [-4.992, 0.0] s
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 90 events and 157 original time points ...
0 bad epochs dropped
Apply short channel correction#
Apply Scholkmann et al 2014 and extract epochs.
od_corrected = mne_nirs.signal_enhancement.short_channel_regression(raw_od)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(od_corrected, ppf=0.1)
raw_haemo = mne_nirs.channels.get_long_channels(raw_haemo)
raw_haemo = raw_haemo.filter(0.05, 0.7, h_trans_bandwidth=0.2,
l_trans_bandwidth=0.02)
epochs_corr = mne.Epochs(raw_haemo, events, event_id=event_dict,
tmin=tmin, tmax=tmax,
reject=reject_criteria, reject_by_annotation=True,
proj=True, baseline=(None, 0), preload=True,
detrend=None, verbose=True)
evoked_dict_corr = {
'Tapping/HbO': epochs_corr['Tapping'].average(picks='hbo'),
'Tapping/HbR': epochs_corr['Tapping'].average(picks='hbr'),
'Control/HbO': epochs_corr['Control'].average(picks='hbo'),
'Control/HbR': epochs_corr['Control'].average(picks='hbr')}
# Rename channels until the encoding of frequency in ch_name is fixed
for condition in evoked_dict_corr:
evoked_dict_corr[condition].rename_channels(lambda x: x[:-4])
Not setting metadata
90 matching events found
Setting baseline interval to [-4.992, 0.0] s
Applying baseline correction (mode: mean)
0 projection items activated
Using data from preloaded Raw for 90 events and 157 original time points ...
Rejecting epoch based on HBO : ['S4_D4 hbo', 'S8_D8 hbo']
Rejecting epoch based on HBO : ['S1_D1 hbo', 'S7_D6 hbo', 'S7_D7 hbo', 'S8_D8 hbo']
Rejecting epoch based on HBO : ['S4_D4 hbo', 'S8_D8 hbo']
3 bad epochs dropped
Plot approaches for comparison#
Plot the average epochs with and without Cui 2010 applied.
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 6))
color_dict = dict(HbO='#AA3377', HbR='b')
styles_dict = dict(Control=dict(linestyle='dashed'))
mne.viz.plot_compare_evokeds(evoked_dict, combine="mean", ci=0.95,
axes=axes[0], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_anti, combine="mean", ci=0.95,
axes=axes[1], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_corr, combine="mean", ci=0.95,
axes=axes[2], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
for column, condition in enumerate(['Original Data',
'With Enhanced Anticorrelation',
'With Short Regression']):
axes[column].set_title('{}'.format(condition))
Plot hemisphere for each approach#
Plot the epoch image for each approach. First we specify the source detector pairs for analysis.
left = [[1, 3], [2, 3], [1, 2], [4, 3]]
right = [[5, 7], [6, 7], [5, 6], [8, 7]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'), left,
on_missing='warning'),
Right_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'), right,
on_missing='warning'))
evoked_dict = {
'Left/HbO': epochs['Tapping/Left'].average(picks='hbo'),
'Left/HbR': epochs['Tapping/Left'].average(picks='hbr'),
'Right/HbO': epochs['Tapping/Right'].average(picks='hbo'),
'Right/HbR': epochs['Tapping/Right'].average(picks='hbr')}
for condition in evoked_dict:
evoked_dict[condition].rename_channels(lambda x: x[:-4])
evoked_dict_anti = {
'Left/HbO': epochs_anti['Tapping/Left'].average(picks='hbo'),
'Left/HbR': epochs_anti['Tapping/Left'].average(picks='hbr'),
'Right/HbO': epochs_anti['Tapping/Right'].average(picks='hbo'),
'Right/HbR': epochs_anti['Tapping/Right'].average(picks='hbr')}
for condition in evoked_dict_anti:
evoked_dict_anti[condition].rename_channels(lambda x: x[:-4])
evoked_dict_corr = {
'Left/HbO': epochs_corr['Tapping/Left'].average(picks='hbo'),
'Left/HbR': epochs_corr['Tapping/Left'].average(picks='hbr'),
'Right/HbO': epochs_corr['Tapping/Right'].average(picks='hbo'),
'Right/HbR': epochs_corr['Tapping/Right'].average(picks='hbr')}
for condition in evoked_dict_corr:
evoked_dict_corr[condition].rename_channels(lambda x: x[:-4])
color_dict = dict(HbO='#AA3377', HbR='b')
styles_dict = dict(Left=dict(linestyle='dashed'))
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(15, 16))
mne.viz.plot_compare_evokeds(evoked_dict, combine="mean", ci=0.95,
picks=groups['Left_ROI'],
axes=axes[0, 0], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict, combine="mean", ci=0.95,
picks=groups['Right_ROI'],
axes=axes[0, 1], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_anti, combine="mean", ci=0.95,
picks=groups['Left_ROI'],
axes=axes[1, 0], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_anti, combine="mean", ci=0.95,
picks=groups['Right_ROI'],
axes=axes[1, 1], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_corr, combine="mean", ci=0.95,
picks=groups['Left_ROI'],
axes=axes[2, 0], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
mne.viz.plot_compare_evokeds(evoked_dict_corr, combine="mean", ci=0.95,
picks=groups['Right_ROI'],
axes=axes[2, 1], colors=color_dict,
styles=styles_dict,
ylim=dict(hbo=[-10, 15]))
for row, condition in enumerate(['Original',
'Anticorrelation',
'Short Regression']):
for column, hemi in enumerate(['Left', 'Right']):
axes[row, column].set_title('{}: {}'.format(condition, hemi))
Total running time of the script: (0 minutes 6.107 seconds)
Estimated memory usage: 9 MB
