moabb.datasets.Beetl2021_A#

class moabb.datasets.Beetl2021_A(phase='final', subjects=None, sessions=None, **kwargs)[source]#

Bases: BaseDataset

Dataset Snapshot

Beetl2021_A

Motor Imagery dataset from BEETL Competition - Dataset A. Part of the NeurIPS 2021 BEETL competition focusing on transfer learning for subject independence and heterogeneous EEG datasets.

Motor Imagery, 4 classes (rest vs left_hand vs right_hand vs feet)

AuthorsXiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet

🇬🇧 Multiple institutions, GB·2022·xiaoxi.wei18@imperial.ac.uk

Motor Imagery Code: Beetl2021-A 3 subjects 1 session 63 ch 500 Hz 4 classes 4.0 s trials CC BY 4.0

Class Labels: rest, left_hand, right_hand, feet

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Overview

Motor Imagery dataset from BEETL Competition - Dataset A.

Dataset description

Dataset A contains data from subjects with 500 Hz sampling rate and 63 EEG channels. In the leaderboard phase, this includes subjects 1-2, while in the final phase it includes subjects 1-3.

Note: for the BEETL competition, there was a leaderboard phase and a final phase. Both phases contained data from two datasets, A and B. However, during leaderboard phase, dataset A contained data from subjects 1-2, while dataset B contained data from subjects 3-5. During the final phase, dataset A contained data from subjects 1-3, while dataset B contained data from subjects 4-5.

Note: for the competition the data is cut into 4 second trials, here the data is concatenated into one session! In order to get the data as provided in the competition, the data has to be cut into 4 second trials.

For the leaderboard phase, the dataset contains only training data, while for the final phase it includes both training and testing data. To learn more about the datasets in detail see To learn more about the competition see

For benchmarking the BEETL competition use phase "final", train on training data benchmark on testing data.

Data is sampled at 500 Hz and contains 63 EEG channels. The data underwent frequency-domain preprocessing using a bandpass filter (1-100 Hz) and a 50 Hz notch filter to attenuate power line interference.

Motor imagery tasks include:

- Rest (label 0)
Left hand (label 1)
Right hand (label 2)
Feet (label 3)

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Citation & Impact

Paper DOI10.48550/arXiv.2202.12950
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Public APICrossref | OpenAlex
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Updated: 2026-03-21 UTC

Stimulus Protocol

4s task window per trial · 4-class motor imagery paradigm · 2 runs/session across 1 sessions

HED Event Tags

HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

4

Agent-action

3

Experimental-stimulus

1

Rest

1

Visual-presentation

1

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

left_hand

Sensory-eventAgent-action

right_hand

Sensory-eventAgent-action

feet

Sensory-eventAgent-action

HED tree view

Tree · rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Tree · left_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand

Tree · right_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand

Tree · feet

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot

Channel Summary

Total channels63

EEG63 (EEG)

Montage10-05

Sampling500 Hz

Filter1-100 Hz bandpass, 50 Hz notch

Notch / line50 Hz

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

Overview

Motor Imagery dataset from BEETL Competition - Dataset A.

Dataset description

Dataset A contains data from subjects with 500 Hz sampling rate and 63 EEG channels. In the leaderboard phase, this includes subjects 1-2, while in the final phase it includes subjects 1-3.

Note: for the BEETL competition, there was a leaderboard phase and a final phase. Both phases contained data from two datasets, A and B. However, during leaderboard phase, dataset A contained data from subjects 1-2, while dataset B contained data from subjects 3-5. During the final phase, dataset A contained data from subjects 1-3, while dataset B contained data from subjects 4-5.

Note: for the competition the data is cut into 4 second trials, here the data is concatenated into one session! In order to get the data as provided in the competition, the data has to be cut into 4 second trials.

For the leaderboard phase, the dataset contains only training data, while for the final phase it includes both training and testing data. To learn more about the datasets in detail see [1]. To learn more about the competition see [2].

For benchmarking the BEETL competition use phase “final”, train on training data benchmark on testing data.

Data is sampled at 500 Hz and contains 63 EEG channels. The data underwent frequency-domain preprocessing using a bandpass filter (1-100 Hz) and a 50 Hz notch filter to attenuate power line interference.

Motor imagery tasks include: - Rest (label 0) - Left hand (label 1) - Right hand (label 2) - Feet (label 3)

phase#

Either “leaderboard” or “final”

Type:: str

References

[1]

Wei, X., Faisal, A. A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S., Jayaram, V., … & Tempczyk, P. (2022, July). 2021 BEETL competition: Advancing transfer learning for subject independence and heterogeneous EEG data sets. In NeurIPS 2021 Competitions and Demonstrations Track (pp. 205-219). PMLR.

[2]

Competition: https://beetl.ai/introduction

Code Examples

from moabb.datasets import Beetl2021_A
dataset = Beetl2021_A()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	4
#Chan	63
#Classes	4
#Trials / class	224
Trials length	4 s
Freq	500 Hz
#Sessions	1
#Runs	1
Total_trials	1490

Participants

Population: healthy

Equipment

Electrodes: EEG
Montage: standard_1005

Preprocessing

Data state: preprocessed
Bandpass filter: 1-100 Hz
Steps: bandpass filter, notch filter

Data Access

DOI: 10.48550/arXiv.2202.12950
Data URL: https://beetl.ai/data
Repository: Figshare

Experimental Protocol

Paradigm: imagery
Task type: motor_imagery_4_class
Feedback: visual

__init__(phase='final', subjects=None, sessions=None, **kwargs)[source]#

Initialize BEETL Dataset A.

Parameters:: phase (str) – Either “leaderboard” (subjects 1-2) or “final” (subjects 1-3)

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)[source]#: Return path to the data files.

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.

Parameters:

subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.
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.
accept (bool) – Accept licence term to download the data, if any. Default: False
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)[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.

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