def load_eeg(events,
rel_start_ms,
rel_stop_ms,
buf_ms=0,
elec_scheme=None,
noise_freq=[58., 62.],
resample_freq=None,
pass_band=None,
use_mirror_buf=False,
demean=False,
do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe that contains eegoffset and eegfile fields
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
buf_ms:
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
elec_scheme: pandas.DataFrame
A dataframe of electrode information, returned by load_elec_info(). If the column 'contact' is in the dataframe,
monopolar electrodes will be loads. If the columns 'contact_1' and 'contact_2' are in the df, bipolar will be
loaded. You may pass in a subset of rows to only load data for electrodes in those rows.
If you do not enter an elec_scheme, all monopolar channels will be loaded (but they will not be labeled with
correct channel tags). Entering a scheme is recommended.
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
use_mirror_buf: bool
If True, the buffer will be data taken from within the rel_start_ms to rel_stop_ms interval,
mirrored and prepended and appended to the timeseries. If False, data outside the rel_start_ms and rel_stop_ms
interval will be read.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions channels x events x time (or bipolar_pairs x events x time)
NOTE: The EEG data is returned with time buffer included. If you included a buffer and want to remove it,
you may use the .remove_buffer() method. EXTRA NOTE: INPUT SECONDS FOR REMOVING BUFFER, NOT MS!!
"""
# add buffer is using
if (buf_ms is not None) and not use_mirror_buf:
actual_start = rel_start_ms - buf_ms
actual_stop = rel_stop_ms + buf_ms
else:
actual_start = rel_start_ms
actual_stop = rel_stop_ms
# load eeg
# Should auto convert to PTSA? Any reason not to?
eeg = CMLReader(subject=events.iloc[0].subject).load_eeg(
events,
rel_start=actual_start,
rel_stop=actual_stop,
scheme=elec_scheme).to_ptsa()
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# add mirror buffer if using. PTSA is expecting this to be in seconds.
if use_mirror_buf:
eeg = eeg.add_mirror_buffer(buf_ms / 1000.)
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
b_filter = ButterworthFilter(eeg,
this_noise_freq,
filt_type='stop',
order=4)
eeg = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
if resample_freq is not None:
r_filter = ResampleFilter(eeg, resample_freq)
eeg = r_filter.filter()
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
# reorder dims to make events first
eeg = make_events_first_dim(eeg)
return eeg
def load_eeg(events,
rel_start_ms,
rel_stop_ms,
buf_ms=0,
elec_scheme=None,
noise_freq=[58., 62.],
resample_freq=None,
pass_band=None,
use_mirror_buf=False,
demean=False,
do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe that contains eegoffset and eegfile fields
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
buf_ms:
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
elec_scheme: pandas.DataFrame
A dataframe of electrode information, returned by load_elec_info(). If the column 'contact' is in the dataframe,
monopolar electrodes will be loads. If the columns 'contact_1' and 'contact_2' are in the df, bipolar will be
loaded. You may pass in a subset of rows to only load data for electrodes in those rows.
If you do not enter an elec_scheme, all monopolar channels will be loaded (but they will not be labeled with
correct channel tags). Entering a scheme is recommended.
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
use_mirror_buf: bool
If True, the buffer will be data taken from within the rel_start_ms to rel_stop_ms interval,
mirrored and prepended and appended to the timeseries. If False, data outside the rel_start_ms and rel_stop_ms
interval will be read.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions channels x events x time (or bipolar_pairs x events x time)
NOTE: The EEG data is returned with time buffer included. If you included a buffer and want to remove it,
you may use the .remove_buffer() method. EXTRA NOTE: INPUT SECONDS FOR REMOVING BUFFER, NOT MS!!
"""
# check if monopolar is possible for this subject
if 'contact' in elec_scheme:
eegfile = np.unique(events.eegfile)[0]
if os.path.splitext(eegfile)[1] == '.h5':
eegfile = f'/protocols/r1/subjects/{events.iloc[0].subject}/experiments/{events.iloc[0].experiment}/sessions/{events.iloc[0].session}/ephys/current_processed/noreref/{eegfile}'
with h5py.File(eegfile, 'r') as f:
if not np.array(f['monopolar_possible'])[0] == 1:
print('Monopolar referencing not possible for {}'.format(
events.iloc[0].subject))
return
# add buffer is using
if (buf_ms is not None) and not use_mirror_buf:
actual_start = rel_start_ms - buf_ms
actual_stop = rel_stop_ms + buf_ms
else:
actual_start = rel_start_ms
actual_stop = rel_stop_ms
# load eeg
eeg = CMLReader(subject=events.iloc[0].subject).load_eeg(
events,
rel_start=actual_start,
rel_stop=actual_stop,
scheme=elec_scheme).to_ptsa()
# now auto cast to float32 to help with memory issues with high sample rate data
eeg.data = eeg.data.astype('float32')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# add mirror buffer if using. PTSA is expecting this to be in seconds.
if use_mirror_buf:
eeg = eeg.add_mirror_buffer(buf_ms / 1000.)
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
for this_chan in range(eeg.shape[1]):
b_filter = ButterworthFilter(eeg[:, this_chan:this_chan + 1],
this_noise_freq,
filt_type='stop',
order=4)
eeg[:, this_chan:this_chan + 1] = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
# pdb.set_trace()
# if resample_freq is not None:
# for this_chan in range(eeg.shape[1]):
# r_filter = ResampleFilter(eeg[:, this_chan:this_chan+1], resample_freq)
# eeg[:, this_chan:this_chan + 1] = r_filter.filter()
if resample_freq is not None:
eeg_resamp = []
for this_chan in range(eeg.shape[1]):
r_filter = ResampleFilter(eeg[:, this_chan:this_chan + 1],
resample_freq)
eeg_resamp.append(r_filter.filter())
coords = {x: eeg[x] for x in eeg.coords.keys()}
coords['time'] = eeg_resamp[0]['time']
coords['samplerate'] = resample_freq
dims = eeg.dims
eeg = TimeSeries.create(np.concatenate(eeg_resamp, axis=1),
resample_freq,
coords=coords,
dims=dims)
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
# reorder dims to make events first
eeg = make_events_first_dim(eeg)
return eeg
