moabb.datasets.Leeuwis2021#

class moabb.datasets.Leeuwis2021(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Leeuwis2021

Single-session left/right-hand motor-imagery EEG from 55 novice BCI users (16 channels, 250 Hz, g.Nautilus), collected to study psychological and cognitive factors in motor-imagery BCI performance.

Imagery, 2 classes (left_hand vs right_hand)

AuthorsNikki Leeuwis, Alissa Paas, Maryam Alimardani

🇳🇱 Tilburg University, Tilburg School of Humanities and Digital Sciences, NL·2021
Imagery Code: Leeuwis2021 55 subjects 1 session 16 ch 250 Hz 2 classes 5.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand

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Overview

Left- vs right-hand motor-imagery EEG dataset

Dataset description

Fifty-five BCI-naive, right-handed students at Tilburg University performed a two-class, left- versus right-hand motor-imagery task in a single session, as part of a study on psychological and cognitive predictors of MI-BCI performance. Each session comprises one calibration run (no feedback) and three feedback runs of 40 trials (20 per class), recorded from 16 channels with a g.Nautilus amplifier at 250 Hz.

Each stored trial spans t = -3 s to +5 s around the cue and carries its class label (-1 = left, +1 = right) in the CSV. This loader concatenates the 40 trials of each run, writes one stimulus event at each cue (t = 0) and marks the joins between stored trials with zero-duration EDGE boundary annotations. The inclusive analysis interval ends at 4.996 s, spanning the 5 s of imagery after the cue.

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Citation & Impact

Stimulus Protocol
../_images/Leeuwis2021.svg

5s task window per trial · 2-class imagery paradigm · 4 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
2
Sensory-event
2
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action

HED tree view

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
Channel Summary
Total channels16
EEG16
Montagestandard_1020
Sampling250 Hz
Referenceright earlobe
Notch / line50 Hz

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

Left- vs right-hand motor-imagery EEG dataset [1].

Dataset description

Fifty-five BCI-naive, right-handed students at Tilburg University performed a two-class, left- versus right-hand motor-imagery task in a single session, as part of a study on psychological and cognitive predictors of MI-BCI performance. Each session comprises one calibration run (no feedback) and three feedback runs of 40 trials (20 per class), recorded from 16 channels with a g.Nautilus amplifier at 250 Hz.

Each stored trial spans t = -3 s to +5 s around the cue and carries its class label (-1 = left, +1 = right) in the CSV. This loader concatenates the 40 trials of each run, writes one stimulus event at each cue (t = 0) and marks the joins between stored trials with zero-duration EDGE boundary annotations. The inclusive analysis interval ends at 4.996 s, spanning the 5 s of imagery after the cue.

References

[1]

Leeuwis, N., Paas, A., and Alimardani, M. (2021). Psychological and Cognitive Factors in Motor Imagery Brain Computer Interfaces. DataverseNL, V1. DOI: https://doi.org/10.34894/Z7ZVOD See also: Leeuwis, N., Paas, A., & Alimardani, M. (2021). Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Frontiers in Human Neuroscience, 15, 634748.

from moabb.datasets import Leeuwis2021
dataset = Leeuwis2021()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

55

#Chan

16

#Classes

2

#Trials / class

80

Trials length

4.996 s

Freq

250 Hz

#Sessions

1

#Runs

4

Total_trials

8800

Participants

  • Population: healthy

  • Age: 20.71 years

  • Handedness: right

  • BCI experience: naive

Equipment

  • Amplifier: g.Nautilus (g.tec Medical Engineering, Austria)

  • Montage: standard_1020

  • Reference: right earlobe

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: visual

  • Stimulus: visual cue

Notes

Added in version 1.8.0.

__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)[source]#

Return the four run CSV paths (calibration, feedback 1-3).

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/.