mne_bids.write_raw_bids#
- mne_bids.write_raw_bids(raw, bids_path, events=None, event_id=None, event_metadata=None, extra_columns_descriptions=None, *, anonymize=None, format='auto', symlink=False, empty_room=None, allow_preload=False, montage=None, acpc_aligned=False, overwrite=False, verbose=None)[source]#
Save raw data to a BIDS-compliant folder structure.
Warning
The original file is simply copied over if the original file format is BIDS-supported for that datatype. Otherwise, this function will convert to a BIDS-supported file format while warning the user. For EEG and iEEG data, conversion will be to BrainVision format; for MEG, conversion will be to FIFF.
mne-bids
will infer the manufacturer information from the file extension. If your file format is non-standard for the manufacturer, please update the manufacturer field in the sidecars manually.
- Parameters:
- raw
mne.io.Raw
The raw data. It must be an instance of
mne.io.Raw
that is not already loaded from disk unlessallow_preload
is explicitly set toTrue
. See warning for theallow_preload
parameter.- bids_path
BIDSPath
The file to write. The
mne_bids.BIDSPath
instance passed here must have thesubject
,task
, androot
attributes set. If thedatatype
attribute is not set, it will be inferred from the recording data type found inraw
. In case of multiple data types, the.datatype
attribute must be set. Example:bids_path = BIDSPath(subject='01', session='01', task='testing', acquisition='01', run='01', datatype='meg', root='/data/BIDS')
This will write the following files in the correct subfolder
root
:sub-01_ses-01_task-testing_acq-01_run-01_meg.fif sub-01_ses-01_task-testing_acq-01_run-01_meg.json sub-01_ses-01_task-testing_acq-01_run-01_channels.tsv sub-01_ses-01_acq-01_coordsystem.json
and the following one if
events
is notNone
:sub-01_ses-01_task-testing_acq-01_run-01_events.tsv
and add a line to the following files:
participants.tsv scans.tsv
Note that the extension is automatically inferred from the raw object.
- eventspath-like |
np.ndarray
|None
Use this parameter to specify events to write to the
*_events.tsv
sidecar file, additionally to the object’sAnnotations
(which are always written). Ifpath-like
, specifies the location of an MNE events file. If an array, the MNE events array (shape:(n_events, 3)
). If a path or an array andraw.annotations
exist, the union ofevents
andraw.annotations
will be written. Mappings from event names to event codes (listed in the third column of the MNE events array) must be specified via theevent_id
parameter; otherwise, an exception is raised. IfAnnotations
are present, their descriptions must be included inevent_id
as well. IfNone
, events will only be inferred from the raw object’sAnnotations
.Note
If specified, writes the union of
events
andraw.annotations
. If you wish to only writeraw.annotations
, passevents=None
. If you want to exclude the events inraw.annotations
from being written, callraw.set_annotations(None)
before invoking this function.Note
Either, descriptions of all event codes must be specified via the
event_id
parameter or each event must be accompanied by a row inevent_metadata
.- event_id
dict
|None
Descriptions or names describing the event codes, if you passed
events
. The descriptions will be written to thetrial_type
column in*_events.tsv
. The dictionary keys correspond to the event description,s and the values to the event codes. You must specify a description for all event codes appearing inevents
. If your data containsAnnotations
, you can use this parameter to assign event codes to each unique annotation description (mapping from description to event code).- event_metadata
pandas.DataFrame
|None
Metadata for each event in
events
. Each row corresponds to an event.- extra_columns_descriptions
dict
|None
A dictionary that maps column names of the
event_metadata
to descriptions. Each column ofevent_metadata
must have a corresponding entry in this.- anonymize
dict
|None
If None (default), no anonymization is performed. If a dictionary, data will be anonymized depending on the dictionary keys:
daysback
is a required key,keep_his
is optional.daysback
intNumber of days by which to move back the recording date in time. In studies with multiple subjects the relative recording date differences between subjects can be kept by using the same number of
daysback
for all subject anonymizations.daysback
should be great enough to shift the date prior to 1925 to conform with BIDS anonymization rules.keep_his
boolIf
False
(default), all subject information next to the recording date will be overwritten as well. IfTrue
, keep subject information apart from the recording date.keep_source
boolWhether to store the name of the
raw
input file in thesource
column ofscans.tsv
. By default, this information is not stored.
- format‘auto’ | ‘BrainVision’ | ‘EDF’ | ‘FIF’ | ‘EEGLAB’
Controls the file format of the data after BIDS conversion. If
'auto'
, MNE-BIDS will attempt to convert the input data to BIDS without a change of the original file format. A conversion to a different file format will then only take place if the original file format lacks some necessary features. Conversion may be forced to BrainVision, EDF, or EEGLAB for (i)EEG, and to FIF for MEG data.- symlink
bool
Instead of copying the source files, only create symbolic links to preserve storage space. This is only allowed when not anonymizing the data (i.e.,
anonymize
must beNone
).Note
Symlinks currently only work with FIFF files. In case of split files, only a link to the first file will be created, and
mne_bids.read_raw_bids()
will correctly handle reading the data again.Note
Symlinks are currently only supported on macOS and Linux. We will add support for Windows 10 at a later time.
- empty_room
mne.io.Raw
|BIDSPath
|None
The empty-room recording to be associated with this file. This is only supported for MEG data. If
Raw
, you may pass raw data that was not preloaded (otherwise, passallow_preload=True
); i.e., it behaves similar to theraw
parameter. The session name will be automatically generated from the raw object’sinfo['meas_date']
. If aBIDSPath
, theroot
attribute must be the same as inbids_path
. PassNone
(default) if you do not wish to specify an associated empty-room recording.Changed in version 0.11: Accepts
Raw
data.- allow_preload
bool
If
True
, allow writing of preloaded raw objects (i.e.,raw.preload
isTrue
). Because the original file is ignored, you must specify whatformat
to write (notauto
).Warning
BIDS was originally designed for unprocessed or minimally processed data. For this reason, by default, we prevent writing of preloaded data that may have been modified. Only use this option when absolutely necessary: for example, manually converting from file formats not supported by MNE or writing preprocessed derivatives. Be aware that these use cases are not fully supported.
- montage
mne.channels.DigMontage
|None
The montage with channel positions if channel position data are to be stored in a format other than “head” (the internal MNE coordinate frame that the data in
raw
is stored in).- acpc_aligned
bool
It is difficult to check whether the T1 scan is ACPC aligned which means that “mri” coordinate space is “ACPC” BIDS coordinate space. So, this flag is required to be True when the digitization data is in “mri” for intracranial data to confirm that the T1 is ACPC-aligned.
- overwrite
bool
Whether to overwrite existing files or data in files. Defaults to
False
.If
True
, any existing files with the same BIDS parameters will be overwritten with the exception of the*_participants.tsv
and*_scans.tsv
files. For these files, parts of pre-existing data that match the current data will be replaced. For*_participants.tsv
, specifically, age, sex and hand fields will be overwritten, while any manually added fields inparticipants.json
andparticipants.tsv
by a user will be retained. IfFalse
, no existing data will be overwritten or replaced.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- raw
- Returns:
Notes
You should ensure that
raw.info['subject_info']
andraw.info['meas_date']
are set to proper (not-None
) values to allow for the correct computation of each participant’s age when creating*_participants.tsv
.This function will convert existing
mne.Annotations
fromraw.annotations
to events. Additionally, any events supplied viaevents
will be written too. To avoid writing of annotations, remove them from the raw file viaraw.set_annotations(None)
before invokingwrite_raw_bids
.To write events encoded in a
STIM
channel, you first need to create the events array manually and pass it to this function:See the documentation of
mne.find_events()
for more information on event extraction fromSTIM
channels.When anonymizing
.edf
files, then the file format for EDF limits how far back we can set the recording date. Therefore, all anonymized EDF datasets will have an internal recording date of01-01-1985
, and the actual recording date will be stored in thescans.tsv
file’sacq_time
column.write_raw_bids
will generate adataset_description.json
file if it does not already exist. Minimal metadata will be written there. If one setsoverwrite
toTrue
here, it will not overwrite an existingdataset_description.json
file. If you need to add more data there, or overwrite it, then you should callmne_bids.make_dataset_description()
directly.When writing EDF or BDF files, all file extensions are forced to be lower-case, in compliance with the BIDS specification.
Examples using mne_bids.write_raw_bids
#
02. Convert MNE sample data to BIDS format
03. Interactive data inspection and bad channel selection
04. Convert EEG data to BIDS format
05. BIDS conversion for group studies
07. Save and load T1-weighted MRI scan along with anatomical landmarks in BIDS
08. Convert iEEG data to BIDS format
09. Manually storing empty room data
10. An introduction to BIDSPath
13. Anonymizing a BIDS dataset
13. Convert NIRS data to BIDS format