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Merge pull request #19 from teonlamont/mne

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ADD ext module for MNE compatibility
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Teon L Brooks
2015-11-06 13:09:54 -05:00
pai ce898c49a8 99eec5bd45
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externals/mne_openbci.py
externo
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"""Conversion tool from OpenBCI to MNE Raw Class"""
# Authors: Teon Brooks <teon.brooks@gmail.com>
#
# License: BSD (3-clause)
import warnings
np = None
try:
import numpy as np
except ImportError:
raise ImportError('Numpy is needed to use function.')
mne = None
try:
from mne.utils import verbose, logger
from mne.io.meas_info import create_info
from mne.io.base import _BaseRaw
except ImportError:
raise ImportError('MNE is needed to use function.')
class RawOpenBCI(_BaseRaw):
"""Raw object from OpenBCI file
Parameters
----------
input_fname : str
Path to the OpenBCI file.
montage : str | None | instance of Montage
Path or instance of montage containing electrode positions.
If None, sensor locations are (0,0,0). See the documentation of
:func:`mne.channels.read_montage` for more information.
eog : list or tuple
Names of channels or list of indices that should be designated
EOG channels. Default is None.
misc : list or tuple
List of indices that should be designated MISC channels.
Default is (-3, -2, -1), which are the accelerator sensors.
stim_channel : int | None
The channel index (starting at 0).
If None (default), there will be no stim channel added.
scale : float
The scaling factor for EEG data. Units for MNE are in volts.
OpenBCI data are typically stored in microvolts. Default scale
factor is 1e-6.
sfreq : int
The sampling frequency of the data. OpenBCI defaults are 250 Hz.
missing_tol : int
The tolerance for interpolating missing samples. Default is 1. If the
number of contiguous missing samples is greater than tolerance, then
values are marked as NaN.
preload : bool
If True, all data are loaded at initialization.
If False, data are not read until save.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
@verbose
def __init__(self, input_fname, montage=None, eog=None,
misc=(-3, -2, -1), stim_channel=None, scale=1e-6, sfreq=250,
missing_tol=1, preload=True, verbose=None):
bci_info = {'missing_tol': missing_tol, 'stim_channel': stim_channel}
if not eog:
eog = list()
if not misc:
misc = list()
nsamps, nchan = self._get_data_dims(input_fname)
last_samps = [nsamps - 1]
ch_names = ['EEG %03d' % num for num in range(1, nchan + 1)]
ch_types = ['eeg'] * nchan
if misc:
misc_names = ['MISC %03d' % ii for ii in range(1, len(misc) + 1)]
misc_types = ['misc'] * len(misc)
for ii, mi in enumerate(misc):
ch_names[mi] = misc_names[ii]
ch_types[mi] = misc_types[ii]
if eog:
eog_names = ['EOG %03d' % ii for ii in range(len(eog))]
eog_types = ['eog'] * len(eog)
for ii, ei in enumerate(eog):
ch_names[ei] = eog_names[ii]
ch_types[ei] = eog_types[ii]
if stim_channel:
ch_names[stim_channel] = 'STI 014'
ch_types[stim_channel] = 'stim'
# fix it for eog and misc marking
info = create_info(ch_names, sfreq, ch_types, montage)
super(RawOpenBCI, self).__init__(info, last_samps=last_samps,
raw_extras=[bci_info],
filenames=[input_fname],
preload=False, verbose=verbose)
# load data
if preload:
self.preload = preload
logger.info('Reading raw data from %s...' % input_fname)
self._data, _ = self._read_segment()
def _read_segment_file(self, data, idx, offset, fi, start, stop,
cals, mult):
"""Read a chunk of raw data"""
input_fname = self._filenames[fi]
data_ = np.genfromtxt(input_fname, delimiter=',', comments='%',
skip_footer=1)
"""
Dealing with the missing data
-----------------------------
When recording with OpenBCI over Bluetooth, it is possible for some of
the data packets, samples, to not be recorded. This does not happen
often but it poses a problem for maintaining proper sampling periods.
OpenBCI data format combats this by providing a counter on the sample
to know which ones are missing.
Solution
--------
Interpolate the missing samples by resampling the surrounding samples.
1. Find where the missing samples are.
2. Deal with the counter reset (resets after cycling a byte).
3. Resample given the diffs.
4. Insert resampled data in the array using the diff indices
(index + 1).
5. If number of missing samples is greater than the missing_tol, Values
are replaced with np.nan.
"""
# counter goes from 0 to 255, maxdiff is 255.
# make diff one like others.
missing_tol = self._raw_extras[fi]['missing_tol']
diff = np.abs(np.diff(data_[:, 0]))
diff = np.mod(diff, 254) - 1
missing_idx = np.where(diff != 0)[0]
missing_samps = diff[missing_idx].astype(int)
if missing_samps.size:
missing_nsamps = np.sum(missing_samps, dtype=int)
missing_cumsum = np.insert(np.cumsum(missing_samps), 0, 0)[:-1]
missing_data = np.empty((missing_nsamps, data_.shape[-1]),
dtype=float)
insert_idx = list()
for idx_, nn, ii in zip(missing_idx, missing_samps,
missing_cumsum):
missing_data[ii:ii + nn] = np.mean(data_[(idx_, idx_ + 1), :])
if nn > missing_tol:
missing_data[ii:ii + nn] *= np.nan
warnings.warn('The number of missing samples exceeded the '
'missing_tol threshold.')
insert_idx.append([idx_] * nn)
insert_idx = np.hstack(insert_idx)
data_ = np.insert(data_, insert_idx, missing_data, axis=0)
# data_ dimensions are samples by channels. transpose for MNE.
data_ = data_[start:stop, 1:].T
data[:, offset:offset + stop - start] = \
np.dot(mult, data_[idx]) if mult is not None else data_[idx]
def _get_data_dims(self, input_fname):
"""Briefly scan the data file for info"""
# raw data formatting is nsamps by nchans + counter
data = np.genfromtxt(input_fname, delimiter=',', comments='%',
skip_footer=1)
diff = np.abs(np.diff(data[:, 0]))
diff = np.mod(diff, 254) - 1
missing_idx = np.where(diff != 0)[0]
missing_samps = diff[missing_idx].astype(int)
nsamps, nchan = data.shape
# add the missing samples
nsamps += sum(missing_samps)
# remove the tracker column
nchan -= 1
del data
return nsamps, nchan
def read_raw_openbci(input_fname, montage=None, eog=None, misc=(-3, -2, -1),
stim_channel=None, scale=1e-6, sfreq=250, missing_tol=1,
preload=True, verbose=None):
"""Raw object from OpenBCI file
Parameters
----------
input_fname : str
Path to the OpenBCI file.
montage : str | None | instance of Montage
Path or instance of montage containing electrode positions.
If None, sensor locations are (0,0,0). See the documentation of
:func:`mne.channels.read_montage` for more information.
eog : list or tuple
Names of channels or list of indices that should be designated
EOG channels. Default is None.
misc : list or tuple
List of indices that should be designated MISC channels.
Default is (-3, -2, -1), which are the accelerator sensors.
stim_channel : str | int | None
The channel name or channel index (starting at 0).
-1 corresponds to the last channel (default).
If None, there will be no stim channel added.
scale : float
The scaling factor for EEG data. Units for MNE are in volts.
OpenBCI data are typically stored in microvolts. Default scale
factor is 1e-6.
sfreq : int
The sampling frequency of the data. OpenBCI defaults are 250 Hz.
missing_tol : int
The tolerance for interpolating missing samples. Default is 1. If the
number of contiguous missing samples is greater than tolerance, then
values are marked as NaN.
preload : bool
If True, all data are loaded at initialization.
If False, data are not read until save.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns
-------
raw : Instance of RawOpenBCI
A Raw object containing OpenBCI data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
raw = RawOpenBCI(input_fname=input_fname, montage=montage, eog=eog,
misc=misc, stim_channel=stim_channel, scale=scale,
sfreq=sfreq, missing_tol=missing_tol, preload=preload,
verbose=verbose)
return raw