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eegbci.py
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eegbci.py
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# Author: Martin Billinger <martin.billinger@tugraz.at>
# License: BSD Style.
import os
from os import path as op
from ..utils import _get_path, _do_path_update
from ...utils import _fetch_file, _url_to_local_path, verbose
EEGMI_URL = 'https://physionet.org/files/eegmmidb/1.0.0/'
@verbose
def data_path(url, path=None, force_update=False, update_path=None,
verbose=None):
"""Get path to local copy of EEGMMI dataset URL.
This is a low-level function useful for getting a local copy of a
remote EEGBCI dataset [1]_ which is available at PhysioNet [2]_.
Parameters
----------
url : str
The dataset to use.
path : None | str
Location of where to look for the EEGBCI data storing location.
If None, the environment variable or config parameter
``MNE_DATASETS_EEGBCI_PATH`` is used. If it doesn't exist, the
"~/mne_data" directory is used. If the EEGBCI dataset
is not found under the given path, the data
will be automatically downloaded to the specified folder.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
path : list of str
Local path to the given data file. This path is contained inside a list
of length one, for compatibility.
Notes
-----
For example, one could do:
>>> from mne.datasets import eegbci
>>> url = 'http://www.physionet.org/physiobank/database/eegmmidb/'
>>> eegbci.data_path(url, os.getenv('HOME') + '/datasets') # doctest:+SKIP
This would download the given EEGBCI data file to the 'datasets' folder,
and prompt the user to save the 'datasets' path to the mne-python config,
if it isn't there already.
References
----------
.. [1] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N.,
Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer
Interface (BCI) System. IEEE TBME 51(6):1034-1043
.. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
Research Resource for Complex Physiologic Signals.
Circulation 101(23):e215-e220
""" # noqa: E501
key = 'MNE_DATASETS_EEGBCI_PATH'
name = 'EEGBCI'
path = _get_path(path, key, name)
destination = _url_to_local_path(url, op.join(path, 'MNE-eegbci-data'))
destinations = [destination]
# Fetch the file
if not op.isfile(destination) or force_update:
if op.isfile(destination):
os.remove(destination)
if not op.isdir(op.dirname(destination)):
os.makedirs(op.dirname(destination))
_fetch_file(url, destination, print_destination=False)
# Offer to update the path
_do_path_update(path, update_path, key, name)
return destinations
@verbose
def load_data(subject, runs, path=None, force_update=False, update_path=None,
base_url=EEGMI_URL, verbose=None): # noqa: D301
"""Get paths to local copies of EEGBCI dataset files.
This will fetch data for the EEGBCI dataset [1]_, which is also
available at PhysioNet [2]_.
Parameters
----------
subject : int
The subject to use. Can be in the range of 1-109 (inclusive).
runs : int | list of int
The runs to use. The runs correspond to:
========= ===================================
run task
========= ===================================
1 Baseline, eyes open
2 Baseline, eyes closed
3, 7, 11 Motor execution: left vs right hand
4, 8, 12 Motor imagery: left vs right hand
5, 9, 13 Motor execution: hands vs feet
6, 10, 14 Motor imagery: hands vs feet
========= ===================================
path : None | str
Location of where to look for the EEGBCI data storing location.
If None, the environment variable or config parameter
``MNE_DATASETS_EEGBCI_PATH`` is used. If it doesn't exist, the
"~/mne_data" directory is used. If the EEGBCI dataset
is not found under the given path, the data
will be automatically downloaded to the specified folder.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_EEGBCI_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
paths : list
List of local data paths of the given type.
Notes
-----
For example, one could do:
>>> from mne.datasets import eegbci
>>> eegbci.load_data(1, [4, 10, 14],\
os.getenv('HOME') + '/datasets') # doctest:+SKIP
This would download runs 4, 10, and 14 (hand/foot motor imagery) runs from
subject 1 in the EEGBCI dataset to the 'datasets' folder, and prompt the
user to save the 'datasets' path to the mne-python config, if it isn't
there already.
References
----------
.. [1] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N.,
Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer
Interface (BCI) System. IEEE TBME 51(6):1034-1043
.. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
Research Resource for Complex Physiologic Signals.
Circulation 101(23):e215-e220
"""
if not hasattr(runs, '__iter__'):
runs = [runs]
data_paths = []
for r in runs:
url = '{u}S{s:03d}/S{s:03d}R{r:02d}.edf'.format(u=base_url,
s=subject, r=r)
data_paths.extend(data_path(url, path, force_update, update_path))
return data_paths
def standardize(raw):
"""Standardize channel positions and names.
Parameters
----------
raw : instance of Raw
The raw data to standardize. Operates in-place.
"""
rename = dict()
for name in raw.ch_names:
std_name = name.strip('.')
std_name = std_name.upper()
if std_name.endswith('Z'):
std_name = std_name[:-1] + 'z'
if std_name.startswith('FP'):
std_name = 'Fp' + std_name[2:]
rename[name] = std_name
raw.rename_channels(rename)