moabb.datasets.BCIComp2020WalkingERP#

class moabb.datasets.BCIComp2020WalkingERP(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

BCIComp2020WalkingERP

BCI Competition 2020 Track 5: P300 oddball ERP detection during walking at 1.6 m/s with simultaneous scalp-EEG, ear-EEG, EOG and forehead IMU recording.

P300 / ERP, 2 classes (NonTarget vs Target)

AuthorsJi-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, Young-Seok Kweon, Jose del R. Millan, Klaus-Robert Muller, Seong-Whan Lee

🇰🇷 Korea University, KR·2022·bcicompetition2020@gmail.com
P300 / ERP Code: BCIComp2020WalkingERP 15 subjects 1 session 56 ch (46 EEG) 100 Hz 2 classes 1.0 s trials CC BY 4.0

Class Labels: NonTarget, Target

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Overview

BCI Competition 2020 Track 5 — ERP during walking (scalp + ear-EEG + IMU).

Dataset from the 2020 International BCI Competition

Dataset Description

Fifteen subjects (S1-S15, aged 19-32, 11 male + 4 female) performed an auditory-style visual oddball ERP paradigm while walking on a treadmill at 1.6 m/s. On every trial a character stimulus was presented for 500 ms (target OOO vs non-target XXX, target ratio 0.2), followed by a 500-1500 ms jittered rest. Each subject completed 300 trials in a single recording; the organizers split them temporally into 180 training / 60 validation / 60 test trials, exposed here as three runs inside a single MOABB session.

Data is released on OSF at 100 Hz, epoched from -190 to +800 ms around stimulus onset (100 samples per trial, 10 ms spacing — the data description PDF rounds this to "-200 to 800 ms", but epo.t in the actual .mat files starts at -190 ms). The recording comprises 32 scalp EEG channels, 14 ear-EEG electrodes, 4 EOG channels, and a 6-axis IMU on the forehead (accelerometer XYZ + gyroscope XYZ).

Channel handling

  • - 32 scalp channels are typed eeg with standard_1005 montage.
  • 4 EOG channels (HEOGL/HEOGR/VEOGU/VEOGL) are typed eog.
  • 14 ear-EEG channels (L1-L10, R1-R8 with gaps) are typed eeg but have no standard-montage positions; set_montage with on_missing="ignore" leaves their coords as NaN.
  • 6 IMU channels are typed misc so classification paradigms that select eeg channels ignore them automatically. They remain in the Raw for users who want to use them for artifact rejection during walking.

Test-run labels are published as a separate answer-sheet XLSX on OSF rather than stored inside the test .mat files. They are embedded in this module as :data:`_TEST_LABELS_RUN2` so the loader can return labelled data for all three runs without a second download.

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/BCIComp2020WalkingERP.svg

1s task window per trial · 2-class p300 / erp paradigm · 3 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget

HED tree view

Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Tree · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Channel Summary
Total channels56
EEG46
MISC6
EOG4
Montage10-05
Sampling100 Hz
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

BCI Competition 2020 Track 5 — ERP during walking (scalp + ear-EEG + IMU).

Dataset from the 2020 International BCI Competition [1].

Dataset Description

Fifteen subjects (S1-S15, aged 19-32, 11 male + 4 female) performed an auditory-style visual oddball ERP paradigm while walking on a treadmill at 1.6 m/s. On every trial a character stimulus was presented for 500 ms (target OOO vs non-target XXX, target ratio 0.2), followed by a 500-1500 ms jittered rest. Each subject completed 300 trials in a single recording; the organizers split them temporally into 180 training / 60 validation / 60 test trials, exposed here as three runs inside a single MOABB session.

Data is released on OSF at 100 Hz, epoched from -190 to +800 ms around stimulus onset (100 samples per trial, 10 ms spacing — the data description PDF rounds this to “-200 to 800 ms”, but epo.t in the actual .mat files starts at -190 ms). The recording comprises 32 scalp EEG channels, 14 ear-EEG electrodes, 4 EOG channels, and a 6-axis IMU on the forehead (accelerometer XYZ + gyroscope XYZ).

Channel handling

  • 32 scalp channels are typed eeg with standard_1005 montage.

  • 4 EOG channels (HEOGL/HEOGR/VEOGU/VEOGL) are typed eog.

  • 14 ear-EEG channels (L1-L10, R1-R8 with gaps) are typed eeg but have no standard-montage positions; set_montage with on_missing="ignore" leaves their coords as NaN.

  • 6 IMU channels are typed misc so classification paradigms that select eeg channels ignore them automatically. They remain in the Raw for users who want to use them for artifact rejection during walking.

Test-run labels are published as a separate answer-sheet XLSX on OSF rather than stored inside the test .mat files. They are embedded in this module as _TEST_LABELS_RUN2 so the loader can return labelled data for all three runs without a second download.

References

[1]

Jeong, J.-H. et al. (2022). 2020 International brain-computer interface competition: A review. Frontiers in Human Neuroscience, 16, 898300. https://doi.org/10.3389/fnhum.2022.898300

from moabb.datasets import BCIComp2020WalkingERP
dataset = BCIComp2020WalkingERP()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

15

#Chan

46

#Trials / class

240 NT / 60 T

Trials length

1 s

Freq

100 Hz

#Sessions

1

Participants

  • Population: healthy

Equipment

  • Montage: standard_1005

Preprocessing

  • Data state: preprocessed

  • Steps: 100 Hz sampling rate, epoched from -190 ms to +800 ms around stimulus

Data Access

Experimental Protocol

  • Paradigm: p300

  • Stimulus: visual character (OOO target, XXX non-target)

__init__(subjects=None, sessions=None)[source]#

Initialize function for the BaseDataset.

property all_subjects#

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None, generate_figures=False)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

  • path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

  • subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

  • verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path="/tmp/bids", subjects=[1])

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None, *, split=None)[source]#

Return local paths for a subject’s split files.

Downloads training + validation + test files for subject via moabb.datasets.download.data_dl(). Returns the path for the requested split (defaults to "training").

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

Download all data from the dataset.

This function is only useful to download all the dataset at once.

When the dataset declares a nemar_id and the download provider is not "upstream", the files come from NEMAR’s sourcedata/ – the original pre-BIDS distribution, byte-identical to what the upstream host serves. On any NEMAR failure this falls back to the dataset’s own downloader with a warning (unless the provider is pinned to "nemar"). See sourcedata_path() and moabb.set_download_provider().

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded. On the NEMAR path each subject is resolved through the deposit’s sourcedata_provenance.json; deposits enriched before that manifest recorded subjects fetch the whole tree.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the 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_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted. Not used on the NEMAR path.

  • accept (bool) – Accept licence term to download the data, if any. Default: False. Only relevant to the dataset’s own downloader; NEMAR mirrors are already public.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str)[source]#

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pandas.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None, n_jobs=1)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {"subject_id": {"session_id": {"run_id": run}}}

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray. This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

  • n_jobs (int) – Number of jobs to run in parallel over subjects (passed to joblib.Parallel). Default 1 (sequential). Per-subject processing (reading, filtering, resampling, epoching) is independent, so this gives a near-linear speedup for datasets with many subjects.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0
sourcedata_path(subject=None, path=None, force_update=False, verbose=None)[source]#

Get the dataset’s original pre-BIDS distribution from NEMAR.

Where data_path() fetches the original files from the upstream host, this fetches the copy NEMAR mirrors under sourcedata/. The files keep their upstream names, so the two are interchangeable in content – but NEMAR stays reachable when the upstream host is slow, rate-limited, behind a bot gate, or retired.

Parameters:

  • subject (int | str | None) – Restrict the download to one subject, resolved through the deposit’s sourcedata_provenance.json. Deposits enriched before that manifest recorded subjects fall back to the whole tree with a warning. When None the whole tree is fetched.

  • path (None | str) – Base path where MOABB stores datasets.

  • force_update (bool) – Re-fetch even when a local copy is present.

  • verbose (bool, str, int, or None) – If not None, override default verbose level.

Returns:

Local path to the sourcedata directory.

Return type:

str

Raises:

  • ValueError – If the dataset declares no nemar_id.

  • moabb.datasets.download.NemarDownloadError – If the download fails or the deposit publishes no sourcedata/.