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import numpy as np
from .peak_finder import peak_finder
from .. import pick_types, pick_channels
from ..utils import logger, verbose
from ..filter import band_pass_filter
from ..epochs import Epochs
@verbose
def find_eog_events(raw, event_id=998, l_freq=1, h_freq=10,
filter_length='10s', ch_name=None, tstart=0,
verbose=None):
"""Locate EOG artifacts
Parameters
----------
raw : instance of Raw
The raw data.
event_id : int
The index to assign to found events.
low_pass : float
Low pass frequency.
high_pass : float
High pass frequency.
filter_length : str | int | None
Number of taps to use for filtering.
ch_name: str | None
If not None, use specified channel(s) for EOG
tstart : float
Start detection after tstart seconds.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
eog_events : array
Events.
"""
# Getting EOG Channel
eog_inds = _get_eog_channel_index(ch_name, raw)
logger.info('EOG channel index for this subject is: %s' % eog_inds)
eog, _ = raw[eog_inds, :]
eog_events = _find_eog_events(eog, event_id=event_id, l_freq=l_freq,
h_freq=h_freq,
sampling_rate=raw.info['sfreq'],
first_samp=raw.first_samp,
filter_length=filter_length,
tstart=tstart)
return eog_events
def _find_eog_events(eog, event_id, l_freq, h_freq, sampling_rate, first_samp,
filter_length='10s', tstart=0.):
"""Helper function"""
logger.info('Filtering the data to remove DC offset to help '
'distinguish blinks from saccades')
# filtering to remove dc offset so that we know which is blink and saccades
fmax = np.minimum(45, sampling_rate / 2.0 - 0.75) # protect Nyquist
filteog = np.array([band_pass_filter(x, sampling_rate, 2, fmax,
filter_length=filter_length)
for x in eog])
temp = np.sqrt(np.sum(filteog ** 2, axis=1))
indexmax = np.argmax(temp)
# easier to detect peaks with filtering.
filteog = band_pass_filter(eog[indexmax], sampling_rate, l_freq, h_freq,
filter_length=filter_length)
# detecting eog blinks and generating event file
logger.info('Now detecting blinks and generating corresponding events')
temp = filteog - np.mean(filteog)
n_samples_start = int(sampling_rate * tstart)
if np.abs(np.max(temp)) > np.abs(np.min(temp)):
eog_events, _ = peak_finder(filteog[n_samples_start:], extrema=1)
else:
eog_events, _ = peak_finder(filteog[n_samples_start:], extrema=-1)
eog_events += n_samples_start
n_events = len(eog_events)
logger.info("Number of EOG events detected : %d" % n_events)
eog_events = np.c_[eog_events + first_samp, np.zeros(n_events),
event_id * np.ones(n_events)]
return eog_events
def _get_eog_channel_index(ch_name, inst):
if isinstance(ch_name, str):
# Check if multiple EOG Channels
if ',' in ch_name:
ch_name = ch_name.split(',')
else:
ch_name = [ch_name]
eog_inds = pick_channels(inst.ch_names, include=ch_name)
if len(eog_inds) == 0:
raise ValueError('%s not in channel list' % ch_name)
else:
logger.info('Using channel %s as EOG channel%s' % (
" and ".join(ch_name),
'' if len(eog_inds) < 2 else 's'))
elif ch_name is None:
eog_inds = pick_types(inst.info, meg=False, eeg=False, stim=False,
eog=True, ecg=False, emg=False, ref_meg=False,
exclude='bads')
if len(eog_inds) == 0:
logger.info('No EOG channels found')
logger.info('Trying with EEG 061 and EEG 062')
eog_inds = pick_channels(inst.ch_names,
include=['EEG 061', 'EEG 062'])
if len(eog_inds) != 2:
raise RuntimeError('EEG 61 or EEG 62 channel not found !!')
else:
raise ValueError('Could not find EOG channel.')
return eog_inds
@verbose
def create_eog_epochs(raw, ch_name=None, event_id=998, picks=None,
tmin=-0.5, tmax=0.5, l_freq=1, h_freq=10,
reject=None, flat=None,
baseline=None, verbose=None):
"""Conveniently generate epochs around EOG artifact events
Parameters
----------
raw : instance of Raw
The raw data
ch_name : str
The name of the channel to use for ECG peak detection.
The argument is mandatory if the dataset contains no ECG channels.
event_id : int
The index to assign to found events
picks : array-like of int | None (default)
Indices of channels to include (if None, all channels
are used).
tmin : float
Start time before event.
tmax : float
End time after event.
l_freq : float
Low pass frequency.
h_freq : float
High pass frequency.
reject : dict | None
Rejection parameters based on peak to peak amplitude.
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.
If reject is None then no rejection is done. You should
use such parameters to reject big measurement artifacts
and not ECG for example
flat : dict | None
Rejection parameters based on flatness of signal
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'
If flat is None then no rejection is done.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
baseline : tuple or list of length 2, or None
The time interval to apply rescaling / baseline correction.
If None do not apply it. If baseline is (a, b)
the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used
and if b is None then b is set to the end of the interval.
If baseline is equal ot (None, None) all the time
interval is used. If None, no correction is applied.
Returns
-------
ecg_epochs : instance of Epochs
Data epoched around ECG r-peaks.
"""
events = find_eog_events(raw, ch_name=ch_name, event_id=event_id,
l_freq=l_freq, h_freq=h_freq)
# create epochs around EOG events
eog_epochs = Epochs(raw, events=events, event_id=event_id,
tmin=tmin, tmax=tmax, proj=False, reject=reject,
flat=flat, picks=picks, baseline=baseline,
preload=True)
return eog_epochs
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