Note
Click here to download the full example code or to run this example in your browser via Binder
05. BIDS conversion for group studies¶
Here, we show how to do BIDS conversion for group studies. We will use the EEG Motor Movement/Imagery Dataset available on the PhysioBank database. We recommend that you go through the more basic BIDS conversion example before checking out this group conversion example: 02. Convert MNE sample data to BIDS format
# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Teon Brooks <teon.brooks@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
Let us import mne_bids
import os.path as op
import shutil
import mne
from mne.datasets import eegbci
from mne_bids import (write_raw_bids, BIDSPath,
get_anonymization_daysback, make_report,
print_dir_tree)
from mne_bids.stats import count_events
And fetch the data for several subjects and runs of a single task.
subject_ids = [1, 2]
# The run numbers in the eegbci are not consecutive ... we follow the online
# documentation to get the 1st, 2nd, and 3rd run of one of the the motor
# imagery task
runs = [
4, # This is run #1 of imagining to open/close left or right fist
8, # ... run #2
12, # ... run #3
]
# map the eegbci run numbers to the number of the run in the motor imagery task
run_map = dict(zip(runs, range(1, 4)))
for subject_id in subject_ids:
eegbci.load_data(subject=subject_id, runs=runs, update_path=True)
# get path to MNE directory with the downloaded example data
mne_data_dir = mne.get_config('MNE_DATASETS_EEGBCI_PATH')
data_dir = op.join(mne_data_dir, 'MNE-eegbci-data')
Let us loop over the subjects and create BIDS-compatible folder
# Make a path where we can save the data to
bids_root = op.join(mne_data_dir, 'eegmmidb_bids_group_conversion')
To ensure the output path doesn’t contain any leftover files from previous tests and example runs, we simply delete it.
Warning
Do not delete directories that may contain important data!
Get a list of the raw objects for this dataset to use their dates to determine the number of daysback to use to anonymize. While we’re looping through the files, also generate the BIDS-compatible names that will be used to save the files in BIDS.
raw_list = list()
bids_list = list()
for subject_id in subject_ids:
for run in runs:
raw_fname = eegbci.load_data(subject=subject_id, runs=run)[0]
raw = mne.io.read_raw_edf(raw_fname)
raw.info['line_freq'] = 50 # specify power line frequency
raw_list.append(raw)
bids_path = BIDSPath(subject=f'{subject_id:03}',
session='01', task='MotorImagery',
run=f'{run_map[run]:02}',
root=bids_root)
bids_list.append(bids_path)
daysback_min, daysback_max = get_anonymization_daysback(raw_list)
for raw, bids_path in zip(raw_list, bids_list):
# By using the same anonymization `daysback` number we can
# preserve the longitudinal structure of multiple sessions for a
# single subject and the relation between subjects. Be sure to
# change or delete this number before putting code online, you
# wouldn't want to inadvertently de-anonymize your data.
#
# Note that we do not need to pass any events, as teh dataset is already
# equipped witn annotations, which wll be converted to BIDS events
# automatically.
write_raw_bids(raw, bids_path,
anonymize=dict(daysback=daysback_min + 2117),
overwrite=True)
Out:
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R04.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R08.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R12.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R04.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R08.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R12.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R04.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/README'...
References
----------
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896
Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-01_events.tsv'...
onset duration trial_type value sample
0.0 4.2 T0 1 0
4.2 4.1 T2 3 672
8.3 4.2 T0 1 1328
12.5 4.1 T1 2 2000
16.6 4.2 T0 1 2656
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-01_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 124.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-01_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-001_ses-01_task-MotorImagery_run-01_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv'...
filename acq_time
eeg/sub-001_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv entry with eeg/sub-001_ses-01_task-MotorImagery_run-01_eeg.edf.
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R08.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-02_events.tsv'...
onset duration trial_type value sample
0.0 4.2 T0 1 0
4.2 4.1 T1 2 672
8.3 4.2 T0 1 1328
12.5 4.1 T2 3 2000
16.6 4.2 T0 1 2656
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-02_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 124.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-02_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-001_ses-01_task-MotorImagery_run-02_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv'...
filename acq_time
eeg/sub-001_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-001_ses-01_task-MotorImagery_run-02_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv entry with eeg/sub-001_ses-01_task-MotorImagery_run-02_eeg.edf.
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S001/S001R12.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-03_events.tsv'...
onset duration trial_type value sample
0.0 4.2 T0 1 0
4.2 4.1 T2 3 672
8.3 4.2 T0 1 1328
12.5 4.1 T1 2 2000
16.6 4.2 T0 1 2656
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19999 = 0.000 ... 124.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-03_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 124.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/eeg/sub-001_ses-01_task-MotorImagery_run-03_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-001_ses-01_task-MotorImagery_run-03_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv'...
filename acq_time
eeg/sub-001_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-001_ses-01_task-MotorImagery_run-02_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-001_ses-01_task-MotorImagery_run-03_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv entry with eeg/sub-001_ses-01_task-MotorImagery_run-03_eeg.edf.
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R04.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
sub-002 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-01_events.tsv'...
onset duration trial_type value sample
0.0 4.1 T0 1 0
4.1 4.1 T1 2 656
8.2 4.1 T0 1 1312
12.3 4.1 T2 3 1968
16.4 4.1 T0 1 2624
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19679 = 0.000 ... 122.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-01_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 122.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-01_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-002_ses-01_task-MotorImagery_run-01_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv'...
filename acq_time
eeg/sub-002_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv entry with eeg/sub-002_ses-01_task-MotorImagery_run-01_eeg.edf.
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R08.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
sub-002 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-02_events.tsv'...
onset duration trial_type value sample
0.0 4.1 T0 1 0
4.1 4.1 T1 2 656
8.2 4.1 T0 1 1312
12.3 4.1 T2 3 1968
16.4 4.1 T0 1 2624
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19679 = 0.000 ... 122.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-02_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 122.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-02_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-002_ses-01_task-MotorImagery_run-02_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv'...
filename acq_time
eeg/sub-002_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-002_ses-01_task-MotorImagery_run-02_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv entry with eeg/sub-002_ses-01_task-MotorImagery_run-02_eeg.edf.
Extracting EDF parameters from /Users/hoechenberger/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S002/S002R12.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv'...
participant_id age sex hand
sub-001 n/a n/a n/a
sub-002 n/a n/a n/a
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.json'...
{
"participant_id": {
"Description": "Unique participant identifier"
},
"age": {
"Description": "Age of the participant at time of testing",
"Units": "years"
},
"sex": {
"Description": "Biological sex of the participant",
"Levels": {
"F": "female",
"M": "male"
}
},
"hand": {
"Description": "Handedness of the participant",
"Levels": {
"R": "right",
"L": "left",
"A": "ambidextrous"
}
}
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-03_events.tsv'...
onset duration trial_type value sample
0.0 4.1 T0 1 0
4.1 4.1 T1 2 656
8.2 4.1 T0 1 1312
12.3 4.1 T2 3 1968
16.4 4.1 T0 1 2624
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/dataset_description.json'...
{
"Name": " ",
"BIDSVersion": "1.4.0",
"DatasetType": "raw",
"Authors": [
"Please cite MNE-BIDS in your publication before removing this (citations in README)"
]
}
Reading 0 ... 19679 = 0.000 ... 122.994 secs...
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-03_eeg.json'...
{
"TaskName": "MotorImagery",
"Manufacturer": "n/a",
"PowerLineFrequency": 50,
"SamplingFrequency": 160.0,
"SoftwareFilters": "n/a",
"RecordingDuration": 122.99375,
"RecordingType": "continuous",
"EEGReference": "n/a",
"EEGGround": "n/a",
"EEGPlacementScheme": "n/a",
"EEGChannelCount": 64,
"EOGChannelCount": 0,
"ECGChannelCount": 0,
"EMGChannelCount": 0,
"MiscChannelCount": 0,
"TriggerChannelCount": 0
}
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/eeg/sub-002_ses-01_task-MotorImagery_run-03_channels.tsv'...
name type units low_cutoff high_cutoff description sampling_frequency status status_description
Fc5. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc3. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc1. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fcz. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Fc2. EEG µV 0.0 80.0 ElectroEncephaloGram 160.0 good n/a
Copying data files to sub-002_ses-01_task-MotorImagery_run-03_eeg.edf
/Users/hoechenberger/Development/mne-bids/mne_bids/write.py:1286: RuntimeWarning: EDF/EDF+/BDF files contain two fields for recording dates.Due to file format limitations, one of these fields only supports 2-digit years. The date for that field will be set to 85 (i.e., 1985), the earliest possible date. EDF/EDF+/BDF reading software should parse the second field for recording dates, which contains the accurately anonymized date as calculated with `daysback`.
warn("EDF/EDF+/BDF files contain two fields for recording dates."
Writing '/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv'...
filename acq_time
eeg/sub-002_ses-01_task-MotorImagery_run-01_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-002_ses-01_task-MotorImagery_run-02_eeg.edf 1919-03-16T16:15:00.000000Z
eeg/sub-002_ses-01_task-MotorImagery_run-03_eeg.edf 1919-03-16T16:15:00.000000Z
Wrote /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv entry with eeg/sub-002_ses-01_task-MotorImagery_run-03_eeg.edf.
Now let’s see the structure of the BIDS folder we created.
Out:
|eegmmidb_bids_group_conversion/
|--- README
|--- dataset_description.json
|--- participants.json
|--- participants.tsv
|--- sub-001/
|------ ses-01/
|--------- sub-001_ses-01_scans.tsv
|--------- eeg/
|------------ sub-001_ses-01_task-MotorImagery_run-01_channels.tsv
|------------ sub-001_ses-01_task-MotorImagery_run-01_eeg.edf
|------------ sub-001_ses-01_task-MotorImagery_run-01_eeg.json
|------------ sub-001_ses-01_task-MotorImagery_run-01_events.tsv
|------------ sub-001_ses-01_task-MotorImagery_run-02_channels.tsv
|------------ sub-001_ses-01_task-MotorImagery_run-02_eeg.edf
|------------ sub-001_ses-01_task-MotorImagery_run-02_eeg.json
|------------ sub-001_ses-01_task-MotorImagery_run-02_events.tsv
|------------ sub-001_ses-01_task-MotorImagery_run-03_channels.tsv
|------------ sub-001_ses-01_task-MotorImagery_run-03_eeg.edf
|------------ sub-001_ses-01_task-MotorImagery_run-03_eeg.json
|------------ sub-001_ses-01_task-MotorImagery_run-03_events.tsv
|--- sub-002/
|------ ses-01/
|--------- sub-002_ses-01_scans.tsv
|--------- eeg/
|------------ sub-002_ses-01_task-MotorImagery_run-01_channels.tsv
|------------ sub-002_ses-01_task-MotorImagery_run-01_eeg.edf
|------------ sub-002_ses-01_task-MotorImagery_run-01_eeg.json
|------------ sub-002_ses-01_task-MotorImagery_run-01_events.tsv
|------------ sub-002_ses-01_task-MotorImagery_run-02_channels.tsv
|------------ sub-002_ses-01_task-MotorImagery_run-02_eeg.edf
|------------ sub-002_ses-01_task-MotorImagery_run-02_eeg.json
|------------ sub-002_ses-01_task-MotorImagery_run-02_events.tsv
|------------ sub-002_ses-01_task-MotorImagery_run-03_channels.tsv
|------------ sub-002_ses-01_task-MotorImagery_run-03_eeg.edf
|------------ sub-002_ses-01_task-MotorImagery_run-03_eeg.json
|------------ sub-002_ses-01_task-MotorImagery_run-03_events.tsv
Now let’s get an overview of the events on the whole dataset
| MotorImagery | |||||
|---|---|---|---|---|---|
| trial_type | T0 | T1 | T2 | ||
| subject | session | run | |||
| 001 | 01 | 01 | 15 | 8 | 7 |
| 02 | 15 | 8 | 7 | ||
| 03 | 15 | 7 | 8 | ||
| 002 | 01 | 01 | 15 | 7 | 8 |
| 02 | 15 | 8 | 7 | ||
| 03 | 15 | 8 | 7 | ||
Now let’s generate a report on the dataset.
dataset_report = make_report(root=bids_root)
print(dataset_report)
Out:
Summarizing participants.tsv /Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/participants.tsv...
Summarizing scans.tsv files [PosixPath('/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-002/ses-01/sub-002_ses-01_scans.tsv'), PosixPath('/Users/hoechenberger/mne_data/eegmmidb_bids_group_conversion/sub-001/ses-01/sub-001_ses-01_scans.tsv')]...
The participant template found: sex were all unknown;
handedness were all unknown; ages all unknown
This dataset was created with BIDS version 1.4.0 by Please cite MNE-BIDS in your
publication before removing this (citations in README). This report was
generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset
consists of 2 participants (sex were all unknown; handedness were all unknown;
ages all unknown)and 1 recording sessions: 01. Data was recorded using a EEG
system sampled at 160.0 Hz with line noise at 50 Hz. There were 6 scans in
total. Recording durations ranged from 122.99 to 124.99 seconds (mean = 123.99,
std = 1.0), for a total of 743.96 seconds of data recorded over all scans. For
each dataset, there were on average 64.0 (std = 0.0) recording channels per
scan, out of which 64.0 (std = 0.0) were used in analysis (0.0 +/- 0.0 were
removed from analysis).
Total running time of the script: ( 0 minutes 0.485 seconds)
