moabb.datasets.BNCI2025_001#

class moabb.datasets.BNCI2025_001(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BNCIBaseDataset

[source]

Dataset Snapshot

BNCI2025_001

EEG dataset investigating simultaneous encoding of speed, distance, and direction in discrete hand reaching movements using a four-direction center-out task

Imagery, 16 classes

AuthorsNitikorn Srisrisawang, Gernot R Müller-Putz

🇦🇹 Institute of Neural Engineering, Graz University of Technology, Austria·2024·gernot.mueller@tugraz.at
Imagery Code: BNCI2025-001 20 subjects 1 session 71 ch (60 EEG) 500 Hz 16 classes 4.0 s trials CC BY 4.0

Class Labels: up_slow_near, up_slow_far, up_fast_near, up_fast_far, down_slow_near, down_slow_far, down_fast_near, down_fast_far, ...

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Overview

BNCI 2025-001 Motor Kinematics Reaching dataset.

Dataset from Srisrisawang & Muller-Putz (2024)

Dataset Description

This dataset investigates how the brain simultaneously encodes multiple kinematic parameters (speed, distance, and direction) during discrete reaching movements. Participants performed a four-direction center-out reaching task with varying speeds (quick/slow) and distances (near/far).

The dataset provides insight into movement planning and execution processes as measured through EEG, enabling research on brain-computer interfaces for motor control and neurorehabilitation applications.

Participants

  • - 20 healthy subjects (12 male, 8 female)
  • Age: 26.1 +/- 4.1 years
  • Handedness: 17 right-handed, 3 left-handed (all used right hand)
  • Location: Institute of Neural Engineering, Graz University of Technology, Austria

Recording Details

  • - Equipment: BrainAmp (Brain Products GmbH)
  • Channels: 60 EEG + 4 EOG = 64 total channels
  • Sampling rate: 500 Hz
  • Reference: Common average reference (CAR) across 55 channels
  • EOG placement: Outer canthi, above/below left eye
  • Electrode positions: Measured with ultrasonic device (ELPOS, Zebris)

Experimental Procedure

  • - 4-direction center-out reaching task
  • 2 speed levels: slow, quick
  • 2 distance levels: near, far
  • 16 conditions total (4 directions x 2 speeds x 2 distances)
  • ~60 trials per condition (~960 total per subject)
  • Trial structure:
  • 1 s preparation period
  • Cue movement (0.4-2.4 s depending on condition)
  • >= 1 s waiting period
  • Movement execution
  • 1 s feedback display
  • 2 s intertrial interval

Event Codes

Events encode the combination of direction, speed, and distance:

  • - up_slow_near (1), up_slow_far (2), up_fast_near (3), up_fast_far (4)
  • down_slow_near (5), down_slow_far (6), down_fast_near (7), down_fast_far (8)
  • left_slow_near (9), left_slow_far (10), left_fast_near (11), left_fast_far (12)
  • right_slow_near (13), right_slow_far (14), right_fast_near (15), right_fast_far (16)
Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/BNCI2025_001.svg

4s task window per trial · 16-class imagery paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags16/16 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
16
Sensory-event
16
up_slow_near
Sensory-eventAgent-action
up_slow_far
Sensory-eventAgent-action
up_fast_near
Sensory-eventAgent-action
up_fast_far
Sensory-eventAgent-action
down_slow_near
Sensory-eventAgent-action
down_slow_far
Sensory-eventAgent-action
down_fast_near
Sensory-eventAgent-action
down_fast_far
Sensory-eventAgent-action
left_slow_near
Sensory-eventAgent-action
left_slow_far
Sensory-eventAgent-action
left_fast_near
Sensory-eventAgent-action
left_fast_far
Sensory-eventAgent-action
right_slow_near
Sensory-eventAgent-action
right_slow_far
Sensory-eventAgent-action
right_fast_near
Sensory-eventAgent-action
right_fast_far
Sensory-eventAgent-action

HED tree view

Tree · up_slow_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Upward
      ├─ Label
      └─ Label
Tree · up_slow_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Upward
      ├─ Label
      └─ Label
Tree · up_fast_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Upward
      ├─ Label
      └─ Label
Tree · up_fast_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Upward
      ├─ Label
      └─ Label
Tree · down_slow_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Downward
      ├─ Label
      └─ Label
Tree · down_slow_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Downward
      ├─ Label
      └─ Label
Tree · down_fast_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Downward
      ├─ Label
      └─ Label
Tree · down_fast_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Downward
      ├─ Label
      └─ Label
Tree · left_slow_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Left
      ├─ Label
      └─ Label
Tree · left_slow_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Left
      ├─ Label
      └─ Label
Tree · left_fast_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Left
      ├─ Label
      └─ Label
Tree · left_fast_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Left
      ├─ Label
      └─ Label
Tree · right_slow_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Right
      ├─ Label
      └─ Label
Tree · right_slow_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Right
      ├─ Label
      └─ Label
Tree · right_fast_near
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Right
      ├─ Label
      └─ Label
Tree · right_fast_far
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Right
      ├─ Label
      └─ Label
Channel Summary
Total channels71
EEG60 (EEG)
MISC7
EOG4
Montage10-05
Sampling500 Hz
Referencecommon average
Filter50 Hz notch
Notch / line50 Hz

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

BNCI 2025-001 Motor Kinematics Reaching dataset.

Dataset from Srisrisawang & Muller-Putz (2024) [1].

Dataset Description

This dataset investigates how the brain simultaneously encodes multiple kinematic parameters (speed, distance, and direction) during discrete reaching movements. Participants performed a four-direction center-out reaching task with varying speeds (quick/slow) and distances (near/far).

The dataset provides insight into movement planning and execution processes as measured through EEG, enabling research on brain-computer interfaces for motor control and neurorehabilitation applications.

Participants

  • 20 healthy subjects (12 male, 8 female)

  • Age: 26.1 +/- 4.1 years

  • Handedness: 17 right-handed, 3 left-handed (all used right hand)

  • Location: Institute of Neural Engineering, Graz University of Technology, Austria

Recording Details

  • Equipment: BrainAmp (Brain Products GmbH)

  • Channels: 60 EEG + 4 EOG = 64 total channels

  • Sampling rate: 500 Hz

  • Reference: Common average reference (CAR) across 55 channels

  • EOG placement: Outer canthi, above/below left eye

  • Electrode positions: Measured with ultrasonic device (ELPOS, Zebris)

Experimental Procedure

  • 4-direction center-out reaching task

  • 2 speed levels: slow, quick

  • 2 distance levels: near, far

  • 16 conditions total (4 directions x 2 speeds x 2 distances)

  • ~60 trials per condition (~960 total per subject)

  • Trial structure:

    • 1 s preparation period

    • Cue movement (0.4-2.4 s depending on condition)

    • >= 1 s waiting period

    • Movement execution

    • 1 s feedback display

    • 2 s intertrial interval

Event Codes

Events encode the combination of direction, speed, and distance: - up_slow_near (1), up_slow_far (2), up_fast_near (3), up_fast_far (4) - down_slow_near (5), down_slow_far (6), down_fast_near (7), down_fast_far (8) - left_slow_near (9), left_slow_far (10), left_fast_near (11), left_fast_far (12) - right_slow_near (13), right_slow_far (14), right_fast_near (15), right_fast_far (16)

References

[1]

Srisrisawang, N., & Muller-Putz, G. R. (2024). Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study. Journal of Neural Engineering, 21(6). https://doi.org/10.1088/1741-2552/ada0ea

from moabb.datasets import BNCI2025_001
dataset = BNCI2025_001()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

20

#Chan

64

#Classes

16

#Trials / class

varies

Trials length

4 s

Freq

500 Hz

#Sessions

1

#Runs

1

Total_trials

varies

Participants

  • Population: Healthy

  • Age: 26.1 years

  • Handedness: {‘right’: 17, ‘left’: 3}

Equipment

  • Amplifier: BrainAmp

  • Electrodes: EEG

  • Montage: standard_1005

  • Reference: common average

Preprocessing

  • Data state: preprocessed with eye artifact correction

  • Bandpass filter: 0.3-80 Hz

  • Steps: low-pass filter at 100 Hz, notch filter at 50 Hz, downsampling to 200 Hz, bad channel rejection and interpolation, bandpass filter 0.3-80 Hz, eye artifact correction via SGEYESUB, ICA with FastICA algorithm, IC artifact removal, low-pass filter at 3 Hz, downsampling to 10 Hz, bad trial rejection, common average reference

  • Re-reference: common average

  • Notes: Frontal channels (AF7, AF3, AFz, AF4, AF8) and EOG removed prior to CAR to reduce residual eye artifacts. Final analysis used 55 channels. Eye blocks recorded separately for SGEYESUB model training. Bad trials rejected based on amplitude >200 µV or standard deviation >5SD. Movement-related bad trials rejected for incorrect direction, no movement, duration <0.2s or >4s, or movement initiated <0.5s after cue stop.

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Task type: discrete reaching

  • Tasks: discrete reaching

  • Feedback: visual (cue color: green for correct, red for incorrect direction)

  • Stimulus: visual cue

Notes

Added in version 1.3.0.

This dataset is notable for its multi-parameter kinematic design, enabling study of how multiple movement parameters are represented simultaneously in EEG activity. The paradigm uses movement execution rather than motor imagery, making it complementary to MI datasets.

The data is compatible with the MOABB motor imagery paradigm for processing purposes, though the underlying task is movement execution.

__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 single 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.

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/.