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|
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import copy as cp
from warnings import warn
import numpy as np
from .. import Epochs, compute_proj_evoked, compute_proj_epochs
from ..utils import logger, verbose
from .. import pick_types
from ..io import make_eeg_average_ref_proj
from .ecg import find_ecg_events
from .eog import find_eog_events
def _safe_del_key(dict_, key):
""" Aux function
Use this function when preparing rejection parameters
instead of directly deleting keys.
"""
if key in dict_:
del dict_[key]
@verbose
def _compute_exg_proj(mode, raw, raw_event, tmin, tmax,
n_grad, n_mag, n_eeg, l_freq, h_freq,
average, filter_length, n_jobs, ch_name,
reject, flat, bads, avg_ref, no_proj, event_id,
exg_l_freq, exg_h_freq, tstart, qrs_threshold,
filter_method, iir_params=None, verbose=None):
"""Compute SSP/PCA projections for ECG or EOG artifacts
Note: raw has to be constructed with preload=True (or string)
Warning: raw will be modified by this function
Parameters
----------
mode : string ('ECG', or 'EOG')
What type of events to detect.
raw : mne.io.Raw
Raw input file.
raw_event : mne.io.Raw or None
Raw file to use for event detection (if None, raw is used).
tmin : float
Time before event in seconds.
tmax : float
Time after event in seconds.
n_grad : int
Number of SSP vectors for gradiometers.
n_mag : int
Number of SSP vectors for magnetometers.
n_eeg : int
Number of SSP vectors for EEG.
l_freq : float | None
Filter low cut-off frequency in Hz.
h_freq : float | None
Filter high cut-off frequency in Hz.
average : bool
Compute SSP after averaging.
filter_length : str | int | None
Number of taps to use for filtering.
n_jobs : int
Number of jobs to run in parallel.
ch_name : string (or None)
Channel to use for ECG event detection.
reject : dict
Epoch rejection configuration (see Epochs).
flat : dict
Epoch flat configuration (see Epochs).
bads : list
List with (additional) bad channels.
avg_ref : bool
Add EEG average reference proj.
no_proj : bool
Exclude the SSP projectors currently in the fiff file.
event_id : int
ID to use for events.
exg_l_freq : float
Low pass frequency applied for filtering EXG channel.
exg_h_freq : float
High pass frequency applied for filtering EXG channel.
tstart : float
Start artifact detection after tstart seconds.
qrs_threshold : float | str
Between 0 and 1. qrs detection threshold. Can also be "auto" to
automatically choose the threshold that generates a reasonable
number of heartbeats (40-160 beats / min). Only for ECG.
filter_method : str
Method for filtering ('iir' or 'fft').
iir_params : dict | None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
proj : list
Computed SSP projectors.
events : ndarray
Detected events.
"""
if not raw.preload:
raise ValueError('raw needs to be preloaded, '
'use preload=True in constructor')
if no_proj:
projs = []
else:
projs = cp.deepcopy(raw.info['projs'])
logger.info('Including %d SSP projectors from raw file'
% len(projs))
if avg_ref:
eeg_proj = make_eeg_average_ref_proj(raw.info)
projs.append(eeg_proj)
if raw_event is None:
raw_event = raw
if mode == 'ECG':
logger.info('Running ECG SSP computation')
events, _, _ = find_ecg_events(raw_event, ch_name=ch_name,
event_id=event_id, l_freq=exg_l_freq,
h_freq=exg_h_freq, tstart=tstart,
qrs_threshold=qrs_threshold,
filter_length=filter_length)
elif mode == 'EOG':
logger.info('Running EOG SSP computation')
events = find_eog_events(raw_event, event_id=event_id,
l_freq=exg_l_freq, h_freq=exg_h_freq,
filter_length=filter_length, ch_name=ch_name,
tstart=tstart)
else:
raise ValueError("mode must be 'ECG' or 'EOG'")
# Check to make sure we actually got at least one useable event
if events.shape[0] < 1:
warn('No %s events found, returning None for projs' % mode)
return None, events
logger.info('Computing projector')
my_info = cp.deepcopy(raw.info)
my_info['bads'] += bads
# Handler rejection parameters
if reject is not None: # make sure they didn't pass None
if len(pick_types(my_info, meg='grad', eeg=False, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(reject, 'grad')
if len(pick_types(my_info, meg='mag', eeg=False, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(reject, 'mag')
if len(pick_types(my_info, meg=False, eeg=True, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(reject, 'eeg')
if len(pick_types(my_info, meg=False, eeg=False, eog=True,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(reject, 'eog')
if flat is not None: # make sure they didn't pass None
if len(pick_types(my_info, meg='grad', eeg=False, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(flat, 'grad')
if len(pick_types(my_info, meg='mag', eeg=False, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(flat, 'mag')
if len(pick_types(my_info, meg=False, eeg=True, eog=False,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(flat, 'eeg')
if len(pick_types(my_info, meg=False, eeg=False, eog=True,
ref_meg=False, exclude='bads')) == 0:
_safe_del_key(flat, 'eog')
# exclude bad channels from projection
picks = pick_types(my_info, meg=True, eeg=True, eog=True, ref_meg=False,
exclude='bads')
raw.filter(l_freq, h_freq, picks=picks, filter_length=filter_length,
n_jobs=n_jobs, method=filter_method, iir_params=iir_params)
epochs = Epochs(raw, events, None, tmin, tmax, baseline=None, preload=True,
picks=picks, reject=reject, flat=flat, proj=True)
epochs.drop_bad_epochs()
if epochs.events.shape[0] < 1:
warn('No good epochs found, returning None for projs')
return None, events
if average:
evoked = epochs.average()
ev_projs = compute_proj_evoked(evoked, n_grad=n_grad, n_mag=n_mag,
n_eeg=n_eeg)
else:
ev_projs = compute_proj_epochs(epochs, n_grad=n_grad, n_mag=n_mag,
n_eeg=n_eeg, n_jobs=n_jobs)
for p in ev_projs:
p['desc'] = mode + "-" + p['desc']
projs.extend(ev_projs)
logger.info('Done.')
return projs, events
@verbose
def compute_proj_ecg(raw, raw_event=None, tmin=-0.2, tmax=0.4,
n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0,
average=False, filter_length='10s', n_jobs=1,
ch_name=None, reject=dict(grad=2000e-13, mag=3000e-15,
eeg=50e-6, eog=250e-6),
flat=None, bads=[], avg_ref=False,
no_proj=False, event_id=999, ecg_l_freq=5, ecg_h_freq=35,
tstart=0., qrs_threshold='auto', filter_method='fft',
iir_params=None, copy=True, verbose=None):
"""Compute SSP/PCA projections for ECG artifacts
Note: raw has to be constructed with preload=True (or string)
Warning: raw will be modified by this function
Parameters
----------
raw : mne.io.Raw
Raw input file.
raw_event : mne.io.Raw or None
Raw file to use for event detection (if None, raw is used).
tmin : float
Time before event in seconds.
tmax : float
Time after event in seconds.
n_grad : int
Number of SSP vectors for gradiometers.
n_mag : int
Number of SSP vectors for magnetometers.
n_eeg : int
Number of SSP vectors for EEG.
l_freq : float | None
Filter low cut-off frequency in Hz.
h_freq : float | None
Filter high cut-off frequency in Hz.
average : bool
Compute SSP after averaging.
filter_length : str | int | None
Number of taps to use for filtering.
n_jobs : int
Number of jobs to run in parallel.
ch_name : string (or None)
Channel to use for ECG detection (Required if no ECG found).
reject : dict
Epoch rejection configuration (see Epochs).
flat : dict
Epoch flat configuration (see Epochs).
bads : list
List with (additional) bad channels.
avg_ref : bool
Add EEG average reference proj.
no_proj : bool
Exclude the SSP projectors currently in the fiff file.
event_id : int
ID to use for events.
ecg_l_freq : float
Low pass frequency applied for filtering ECG channel.
ecg_h_freq : float
High pass frequency applied for filtering ECG channel.
tstart : float
Start artifact detection after tstart seconds.
qrs_threshold : float | str
Between 0 and 1. qrs detection threshold. Can also be "auto" to
automatically choose the threshold that generates a reasonable
number of heartbeats (40-160 beats / min).
filter_method : str
Method for filtering ('iir' or 'fft').
iir_params : dict | None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
copy : bool
If False, filtering raw data is done in place. Defaults to True.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
proj : list
Computed SSP projectors.
ecg_events : ndarray
Detected ECG events.
"""
if copy is True:
raw = raw.copy()
projs, ecg_events = _compute_exg_proj('ECG', raw, raw_event, tmin, tmax,
n_grad, n_mag, n_eeg, l_freq, h_freq,
average, filter_length, n_jobs,
ch_name, reject, flat, bads, avg_ref,
no_proj, event_id, ecg_l_freq,
ecg_h_freq, tstart, qrs_threshold,
filter_method, iir_params)
return projs, ecg_events
@verbose
def compute_proj_eog(raw, raw_event=None, tmin=-0.2, tmax=0.2,
n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0,
average=False, filter_length='10s', n_jobs=1,
reject=dict(grad=2000e-13, mag=3000e-15, eeg=500e-6,
eog=np.inf), flat=None, bads=[],
avg_ref=False, no_proj=False, event_id=998, eog_l_freq=1,
eog_h_freq=10, tstart=0., filter_method='fft',
iir_params=None, ch_name=None, copy=True, verbose=None):
"""Compute SSP/PCA projections for EOG artifacts
Note: raw has to be constructed with preload=True (or string)
Warning: raw will be modified by this function
Parameters
----------
raw : mne.io.Raw
Raw input file.
raw_event : mne.io.Raw or None
Raw file to use for event detection (if None, raw is used).
tmin : float
Time before event in seconds.
tmax : float
Time after event in seconds.
n_grad : int
Number of SSP vectors for gradiometers.
n_mag : int
Number of SSP vectors for magnetometers.
n_eeg : int
Number of SSP vectors for EEG.
l_freq : float | None
Filter low cut-off frequency in Hz.
h_freq : float | None
Filter high cut-off frequency in Hz.
average : bool
Compute SSP after averaging.
preload : string (or True)
Temporary file used during computaion.
filter_length : str | int | None
Number of taps to use for filtering.
n_jobs : int
Number of jobs to run in parallel.
reject : dict
Epoch rejection configuration (see Epochs).
flat : dict
Epoch flat configuration (see Epochs).
bads : list
List with (additional) bad channels.
avg_ref : bool
Add EEG average reference proj.
no_proj : bool
Exclude the SSP projectors currently in the fiff file.
event_id : int
ID to use for events.
eog_l_freq : float
Low pass frequency applied for filtering E0G channel.
eog_h_freq : float
High pass frequency applied for filtering E0G channel.
tstart : float
Start artifact detection after tstart seconds.
filter_method : str
Method for filtering ('iir' or 'fft').
iir_params : dict | None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
copy : bool
If False, filtering raw data is done in place. Defaults to True.
ch_name: str | None
If not None, specify EOG channel name.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
proj: list
Computed SSP projectors.
eog_events: ndarray
Detected EOG events.
"""
if copy is True:
raw = raw.copy()
projs, eog_events = _compute_exg_proj('EOG', raw, raw_event, tmin, tmax,
n_grad, n_mag, n_eeg, l_freq, h_freq,
average, filter_length, n_jobs,
ch_name, reject, flat, bads, avg_ref,
no_proj, event_id, eog_l_freq,
eog_h_freq, tstart,
qrs_threshold='auto',
filter_method=filter_method,
iir_params=iir_params)
return projs, eog_events
|
