Convert EEG data to BIDS format

In this example, we use MNE-BIDS to create a BIDS-compatible directory of EEG data. Specifically, we will follow these steps:

  1. Download some EEG data from the PhysioBank database.
  2. Load the data, extract information, and save in a new BIDS directory
  3. Check the result and compare it with the standard
# Authors: Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# License: BSD (3-clause)

We are importing everything we need for this example:

import os
import shutil as sh

import mne
from mne.datasets import eegbci
from mne.io.edf.edf import read_annotations_edf

from mne_bids import write_raw_bids, make_bids_basename
from mne_bids.utils import print_dir_tree

Step 1: Download the data

First, we need some data to work with. We will use the EEG Motor Movement/Imagery Dataset available on the PhysioBank database.

The data consists of 109 volunteers performing 14 experimental runs each. For each subject, there were two baseline tasks (1. eyes open, 2. eyes closed) as well as four different motor imagery tasks. For the present example, we will show how to format the data for two subjects and selected tasks to comply with the Brain Imaging Data Structure (BIDS <http://bids.neuroimaging.io/>).

Conveniently, there is already a data loading function available with MNE-Python:

# Make a directory to save the data to
home = os.path.expanduser('~')
mne_dir = os.path.join(home, 'mne_data')
if not os.path.exists(mne_dir):
    os.makedirs(mne_dir)

# Define which tasks we want to download.
tasks = [2,  # This is 2 minutes of eyes closed rest
         4,  # This is run #1 of imagining to close left or right fist
         12]  # This is run #2 of imagining to close left or right fist

# Download the data for subjects 1 and 2
for subj_idx in [1, 2]:
    eegbci.load_data(subject=subj_idx,
                     runs=tasks,
                     path=mne_dir,
                     update_path=True)

Out:

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R02.edf (1.2 MB)

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R04.edf (2.5 MB)

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S001/S001R12.edf (2.5 MB)

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S002/S002R02.edf (1.2 MB)

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S002/S002R04.edf (2.4 MB)

Downloading http://www.physionet.org/physiobank/database/eegmmidb/S002/S002R12.edf (2.4 MB)

Let’s see whether the data has been downloaded using a quick visualization of the directory tree.

data_dir = os.path.join(mne_dir, 'MNE-eegbci-data')
print_dir_tree(data_dir)

Out:

|------------ MNE-eegbci-data
|--------------- physiobank
|------------------ database
|--------------------- eegmmidb
|------------------------ S001
|--------------------------- S001R02.edf
|--------------------------- S001R12.edf
|--------------------------- S001R04.edf
|------------------------ S002
|--------------------------- S002R12.edf
|--------------------------- S002R02.edf
|--------------------------- S002R04.edf

The data are in the European Data Format ‘.edf’, which is good for us because next to the BrainVision format, EDF is one of the recommended file formats for EEG BIDS. However, apart from the data format, we need to build a directory structure and supply meta data files to properly bidsify this data.

Step 2: Formatting as BIDS

Let’s start by formatting a single subject. We are reading the data using MNE-Python’s io module and the read_raw_edf function. Note that we must use preload=False, the default in MNE-Python. It prevents the data from being loaded and modified when converting to BIDS.

edf_path = eegbci.load_data(subject=1, runs=2)[0]
raw = mne.io.read_raw_edf(edf_path, preload=False, stim_channel=None)

Out:

Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

The annotations stored in the file must be read in separately and converted into a 2D numpy array of events that is compatible with MNE.

annot = read_annotations_edf(edf_path)
raw.set_annotations(annot)
events, event_id = mne.events_from_annotations(raw)

print(raw)

Out:

Used Annotations descriptions: ['T0']
<RawEDF  |  S001R02.edf, n_channels x n_times : 65 x 9760 (61.0 sec), ~124 kB, data not loaded>

With this step, we have everything to start a new BIDS directory using our data. To do that, we can use the function write_raw_bids Generally, write_raw_bids tries to extract as much meta data as possible from the raw data and then formats it in a BIDS compatible way. write_raw_bids takes a bunch of inputs, most of which are however optional. The required inputs are:

  • raw
  • bids_basename
  • output_path

… as you can see in the docstring:

print(write_raw_bids.__doc__)

Out:

Walk over a folder of files and create BIDS compatible folder.

    .. warning:: The original files are simply copied over. This function
                 cannot convert modify data files from one format to another.
                 Modification of the original data files is not allowed.

    Parameters
    ----------
    raw : instance of mne.io.Raw
        The raw data. It must be an instance of mne.Raw. The data should not be
        loaded on disk, i.e., raw.preload must be False.
    bids_basename : str
        The base filename of the BIDS compatible files. Typically, this can be
        generated using make_bids_basename.
        Example: sub-01_ses-01_task-testing_acq-01_run-01
        This will write the following files in the correct subfolder
        of output_path:
            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_task-testing_acq-01_run-01_coordsystem.json
        and the following one if events_data is not None
            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 modality 'meg' is automatically inferred from the raw
        object and extension '.fif' is copied from raw.filenames.
    output_path : str
        The path of the root of the BIDS compatible folder. The session and
        subject specific folders will be populated automatically by parsing
        bids_basename.
    events_data : str | array | None
        The events file. If a string, a path to the events file. If an array,
        the MNE events array (shape n_events, 3). If None, events will be
        inferred from the stim channel using `mne.find_events`.
    event_id : dict | None
        The event id dict used to create a 'trial_type' column in events.tsv
    overwrite : bool
        Whether to overwrite existing files or data in files.
        Defaults to False.
        If overwrite is 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.
        If overwrite is False, no existing data will be overwritten or
        replaced.
    verbose : bool
        If verbose is True, this will print a snippet of the sidecar files. If
        False, no content will be printed.

    Notes
    -----
    For the participants.tsv file, the raw.info['subjects_info'] should be
    updated and raw.info['meas_date'] should not be None to compute the age
    of the participant correctly.

We loaded ‘S001R02.edf’, which corresponds to subject 1 in the second task. The second task was to rest with closed eyes.

subject_id = '001'  # zero padding to account for >100 subjects in this dataset
task = 'resteyesclosed'
raw_file = raw
output_path = os.path.join(home, 'mne_data', 'eegmmidb_bids')

Now we just need to specify a few more EEG details to get something sensible:

# Brief description of the event markers present in the data. This will become
# the `trial_type` column in our BIDS `events.tsv`. We know about the event
# meaning from the documentation on PhysioBank
trial_type = {'rest': 0, 'imagine left fist': 1, 'imagine right fist': 2}

# Now convert our data to be in a new BIDS dataset.
bids_basename = make_bids_basename(subject=subject_id, task=task)
write_raw_bids(raw_file, bids_basename, output_path, event_id=trial_type,
               events_data=events, overwrite=True)

Out:

Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Creating folder: /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration         trial_type  event_value  event_sample
0    0.0       0.0  imagine left fist            1             0

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 9759  =      0.000 ...    60.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "Manufacturer": "n/a",
    "PowerLineFrequency": 50,
    "SamplingFrequency": 160.0,
    "SoftwareFilters": "n/a",
    "RecordingDuration": 60.99375,
    "RecordingType": "continuous",
    "EEGReference": "n/a",
    "EEGGround": "n/a",
    "EEGPlacementScheme": "n/a",
    "EEGChannelCount": 64,
    "EOGChannelCount": 0,
    "ECGChannelCount": 0,
    "EMGChannelCount": 0,
    "MiscChannelCount": 0,
    "TriggerChannelCount": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf

What does our fresh BIDS directory look like?

print_dir_tree(output_path)

Out:

|------------ eegmmidb_bids
|--------------- dataset_description.json
|--------------- participants.tsv
|--------------- sub-001
|------------------ sub-001_scans.tsv
|------------------ eeg
|--------------------- sub-001_task-resteyesclosed_events.tsv
|--------------------- sub-001_task-resteyesclosed_eeg.json
|--------------------- sub-001_task-resteyesclosed_eeg.edf
|--------------------- sub-001_task-resteyesclosed_channels.tsv

Looks good so far, let’s convert the data for all tasks and subjects.

# Start with a clean directory
sh.rmtree(output_path)

# Some initial information that we found in the PhysioBank documentation
task_names = {2: 'resteyesclosed',
              4: 'imaginefists',  # run 1
              12: 'imaginefists'  # run 2
              }

run_mapping = {2: None,  # for resting eyes closed task, there was only one run
               4: '1',
               12: '2'
               }

# Now go over subjects and *bidsify*
for subj_idx in [1, 2]:
    for task_idx in [2, 4, 12]:
        # Load the data
        edf_path = eegbci.load_data(subject=subj_idx, runs=task_idx)[0]

        raw = mne.io.read_raw_edf(edf_path, preload=False, stim_channel=None)
        annot = read_annotations_edf(edf_path)
        raw.set_annotations(annot)
        events, event_id = mne.events_from_annotations(raw)

        make_bids_basename(
            subject='{:03}'.format(subj_idx), task=task_names[task_idx],
            run=run_mapping[task_idx])
        write_raw_bids(raw, bids_basename, output_path, event_id=trial_type,
                       events_data=events, overwrite=True)

Out:

Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Creating folder: /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration         trial_type  event_value  event_sample
0    0.0       0.0  imagine left fist            1             0

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 9759  =      0.000 ...    60.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "Manufacturer": "n/a",
    "PowerLineFrequency": 50,
    "SamplingFrequency": 160.0,
    "SoftwareFilters": "n/a",
    "RecordingDuration": 60.99375,
    "RecordingType": "continuous",
    "EEGReference": "n/a",
    "EEGGround": "n/a",
    "EEGPlacementScheme": "n/a",
    "EEGChannelCount": 64,
    "EOGChannelCount": 0,
    "ECGChannelCount": 0,
    "EMGChannelCount": 0,
    "MiscChannelCount": 0,
    "TriggerChannelCount": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R04.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0', 'T2', 'T1']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R04.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration          trial_type  event_value  event_sample
0    0.0       0.0   imagine left fist            1             0
1    4.2       0.0  imagine right fist            2           672
2    8.3       0.0   imagine left fist            1          1328
3   12.5       0.0                 NaN            3          2000
4   16.6       0.0   imagine left fist            1          2656

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 19999  =      0.000 ...   124.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "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": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R12.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0', 'T2', 'T1']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R12.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration          trial_type  event_value  event_sample
0    0.0       0.0   imagine left fist            1             0
1    4.2       0.0  imagine right fist            2           672
2    8.3       0.0   imagine left fist            1          1328
3   12.5       0.0                 NaN            3          2000
4   16.6       0.0   imagine left fist            1          2656

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 19999  =      0.000 ...   124.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "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": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R02.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration         trial_type  event_value  event_sample
0    0.0       0.0  imagine left fist            1             0

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 9759  =      0.000 ...    60.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "Manufacturer": "n/a",
    "PowerLineFrequency": 50,
    "SamplingFrequency": 160.0,
    "SoftwareFilters": "n/a",
    "RecordingDuration": 60.99375,
    "RecordingType": "continuous",
    "EEGReference": "n/a",
    "EEGGround": "n/a",
    "EEGPlacementScheme": "n/a",
    "EEGChannelCount": 64,
    "EOGChannelCount": 0,
    "ECGChannelCount": 0,
    "EMGChannelCount": 0,
    "MiscChannelCount": 0,
    "TriggerChannelCount": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R04.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0', 'T1', 'T2']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R04.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration          trial_type  event_value  event_sample
0    0.0       0.0   imagine left fist            1             0
1    4.1       0.0  imagine right fist            2           656
2    8.2       0.0   imagine left fist            1          1312
3   12.3       0.0                 NaN            3          1968
4   16.4       0.0   imagine left fist            1          2624

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 19679  =      0.000 ...   122.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "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": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R12.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...
Used Annotations descriptions: ['T0', 'T1', 'T2']
Extracting EDF parameters from /home/circleci/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S002/S002R12.edf...
EDF file detected
EDF annotations detected (consider using raw.find_edf_events() to extract them)
Setting channel info structure...
Creating raw.info structure...

Writing '/home/circleci/mne_data/eegmmidb_bids/participants.tsv'...

  participant_id  age  sex group
0        sub-001  NaN  NaN   NaN

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/sub-001_scans.tsv'...

                                  filename             acq_time
0  eeg/sub-001_task-resteyesclosed_eeg.edf  2009-08-12T16:15:00

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_events.tsv'...

   onset  duration          trial_type  event_value  event_sample
0    0.0       0.0   imagine left fist            1             0
1    4.1       0.0  imagine right fist            2           656
2    8.2       0.0   imagine left fist            1          1312
3   12.3       0.0                 NaN            3          1968
4   16.4       0.0   imagine left fist            1          2624

Writing '/home/circleci/mne_data/eegmmidb_bids/dataset_description.json'...

{
    "Name": " ",
    "BIDSVersion": "1.1.1 (draft)"
}
Reading 0 ... 19679  =      0.000 ...   122.994 secs...

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.json'...

{
    "TaskName": "resteyesclosed",
    "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": 1
}

Writing '/home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_channels.tsv'...

   name type units           description  sampling_frequency  low_cutoff  \
0  Fc5.  EEG     V  ElectroEncephaloGram               160.0         0.0
1  Fc3.  EEG     V  ElectroEncephaloGram               160.0         0.0
2  Fc1.  EEG     V  ElectroEncephaloGram               160.0         0.0
3  Fcz.  EEG     V  ElectroEncephaloGram               160.0         0.0
4  Fc2.  EEG     V  ElectroEncephaloGram               160.0         0.0

   high_cutoff status
0         80.0   good
1         80.0   good
2         80.0   good
3         80.0   good
4         80.0   good
Copying data files to /home/circleci/mne_data/eegmmidb_bids/sub-001/eeg/sub-001_task-resteyesclosed_eeg.edf

Step 3: Check and compare with standard

Now we have written our BIDS directory.

print_dir_tree(output_path)

Out:

|------------ eegmmidb_bids
|--------------- dataset_description.json
|--------------- participants.tsv
|--------------- sub-001
|------------------ sub-001_scans.tsv
|------------------ eeg
|--------------------- sub-001_task-resteyesclosed_events.tsv
|--------------------- sub-001_task-resteyesclosed_eeg.json
|--------------------- sub-001_task-resteyesclosed_eeg.edf
|--------------------- sub-001_task-resteyesclosed_channels.tsv

MNE-BIDS has created a suitable directory structure for us, and among other meta data files, it started an events.tsv and channels.tsv and made an initial dataset_description on top!

Now it’s time to manually check the BIDS directory and the meta files to add all the information that MNE-BIDS could not infer. For instance, you must describe EEGReference and EEGGround yourself. It’s easy to find these by searching for “n/a” in the sidecar files.

Remember that there is a convenient javascript tool to validate all your BIDS directories called the “BIDS-validator”, available as a web version and a command line tool:

Web version: https://bids-standard.github.io/bids-validator/

Command line tool: https://www.npmjs.com/package/bids-validator

Total running time of the script: ( 0 minutes 2.376 seconds)

Gallery generated by Sphinx-Gallery