moabb.datasets.BNCI2019_001#

class moabb.datasets.BNCI2019_001(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

BNCI2019_001

This dataset investigates whether attempted arm and hand movements in persons with spinal cord injury can be decoded from low-frequency EEG signals (MRCPs). The study includes offline 5-class classification and online proof-of-concept for self-paced movement detection.

Motor Imagery, 5 classes (supination vs pronation vs hand_open vs palmar_grasp vs lateral_grasp)

AuthorsPatrick Ofner, Andreas Schwarz, Joana Pereira, Daniela Wyss, Renate Wildburger, Gernot R. MΓΌller-Putz

πŸ‡¦πŸ‡Ήβ€‚Graz University of Technology, AustriaΒ·2019Β·gernot.mueller@tugraz.at
Motor Imagery Code: BNCI2019-001 10 subjects 1 session 61 ch 256 Hz 5 classes 5.0 s trials CC BY 4.0

Class Labels: supination, pronation, hand_open, palmar_grasp, lateral_grasp

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Overview

BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients.

Dataset from

Dataset Description

This dataset consists of EEG recordings from 10 participants with cervical spinal cord injury (SCI) performing attempted hand and arm movements.

Participants attempted five movement types: supination, pronation, hand open, palmar grasp, and lateral grasp.

Participants

  • - 10 participants with cervical spinal cord injury
  • Age range: 20-78 years (mean 49.8, SD 17.6)
  • Gender: 9 male, 1 female
  • Handedness: All right-handed

Recording Details

  • - Channels: 61 EEG + 3 EOG electrodes
  • Sampling rate: 256 Hz
  • Reference: Left earlobe

Motor Imagery Classes

  • - supination (776): Forearm supination
  • pronation (777): Forearm pronation
  • hand_open (779): Hand opening movement
  • palmar_grasp (925): Palmar (power) grasp
  • lateral_grasp (926): Lateral (key) grasp
Open in Overview tab β†’

Citation & Impact

Stimulus Protocol
../_images/BNCI2019_001.svg

5s task window per trial Β· 5-class motor imagery paradigm Β· 9 runs/session across 1 sessions

HED Event Tags
HED tags5/5 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
5
Sensory-event
5
supination
Sensory-eventAgent-action
pronation
Sensory-eventAgent-action
hand_open
Sensory-eventAgent-action
palmar_grasp
Sensory-eventAgent-action
lateral_grasp
Sensory-eventAgent-action

HED tree view

Tree Β· supination
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      β”œβ”€ Turn
      β”œβ”€ Forearm
      └─ Label
Tree Β· pronation
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      β”œβ”€ Turn
      β”œβ”€ Forearm
      └─ Label
Tree Β· hand_open
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      β”œβ”€ Open
      └─ Hand
Tree Β· palmar_grasp
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      β”œβ”€ Grasp
      └─ Hand
Tree Β· lateral_grasp
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      β”œβ”€ Grasp
      β”œβ”€ Hand
      └─ Label
Channel Summary
Total channels61
EEG61 (active electrode)
EOG3
Montage10-5
Sampling256 Hz
Referenceleft earlobe
Filter50 Hz notch, 0.01-100 Hz bandpass
Notch / line50 Hz

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

BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients.

Dataset from [1].

Dataset Description

This dataset consists of EEG recordings from 10 participants with cervical spinal cord injury (SCI) performing attempted hand and arm movements.

Participants attempted five movement types: supination, pronation, hand open, palmar grasp, and lateral grasp.

Participants

  • 10 participants with cervical spinal cord injury

  • Age range: 20-78 years (mean 49.8, SD 17.6)

  • Gender: 9 male, 1 female

  • Handedness: All right-handed

Recording Details

  • Channels: 61 EEG + 3 EOG electrodes

  • Sampling rate: 256 Hz

  • Reference: Left earlobe

Motor Imagery Classes

  • supination (776): Forearm supination

  • pronation (777): Forearm pronation

  • hand_open (779): Hand opening movement

  • palmar_grasp (925): Palmar (power) grasp

  • lateral_grasp (926): Lateral (key) grasp

References

[1]

Ofner, P. et al. (2019). Attempted arm and hand movements can be decoded from low-frequency EEG from persons with spinal cord injury. Scientific Reports, 9(1), 7134. https://doi.org/10.1038/s41598-019-43594-9

from moabb.datasets import BNCI2019_001
dataset = BNCI2019_001()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

10

#Chan

64

#Classes

5

#Trials / class

varies

Trials length

3 s

Freq

256 Hz

#Sessions

1

#Runs

9

Total_trials

varies

Participants

  • Population: patients

  • Clinical population: spinal cord injury

  • Age: 49.8 (range: 20-78) years

  • Handedness: right-handed (all participants originally)

Equipment

  • Amplifier: g.tec

  • Electrodes: active electrode

  • Montage: 10-5

  • Reference: left earlobe

Preprocessing

  • Data state: raw (GDF format)

  • Bandpass filter: 0.01-100 Hz

  • Steps: bandpass filter, notch filter, ICA, artifact rejection

  • Re-reference: CAR

  • Notes: Noisy channels were visually inspected and removed. AFz was removed by default as it is sensitive to eye blinks and eye movements. ICA was performed on 0.3-70 Hz filtered signals using extended infomax. PCA dimensionality reduction retained 99% variance. Artifact-contaminated ICs (muscle and eye-related) were removed. Trials with values above/below Β±100 ΞΌV, abnormal joint probabilities, or abnormal kurtosis (5x SD threshold) were rejected. Final analysis used 0.3-3 Hz bandpass filter.

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Task type: attempted movement

  • Tasks: hand_open, palmar_grasp, lateral_grasp, pronation, supination

  • Feedback: visual feedback (online paradigm only - movement icon displayed when movement detected)

  • Stimulus: visual cue

Dataset summary

Name

#Subj

#Chan

#Trials/class

Trials length

Sampling Rate

#Sessions

BNCI2019_001

10

61+3EOG

72 per class

3s

256Hz

1

Notes

Added in version 1.2.0.

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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 paths to data files for a given subject.

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 β€œ~/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 β€œfixed” 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