EEGdash›NeMAR›NM000305

Iss. 305 · 28 subjects · 912 recordings · CC-BY-4.0

Dataset Brief · NETBCI2026

NM000305: eeg dataset, 28 subjects#

NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training

Access recordings and metadata through EEGDash.

Citation: Marie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, Denis Schwartz, Nathalie George, Mario Chavez, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani (2026). NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training. 10.82901/nemar.nm000305

Modality: eeg Subjects: 28 Recordings: 912 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

28-participant EEG dataset — NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training.

Data & curation Marie-Constance Corsi · Christophe Gitton · Juliana Gonzalez-Astudillo · Ari E. Kahn · Laurent Hugueville · Denis Schwartz · …
Senior author Fabrizio De Vico Fallani
Year 2026 · Distributed via NeMAR

EEG · 74 ch250 HzBIDS 1.9.02 tasks4 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000305

dataset = NM000305(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000305(cache_dir="./data", subject="01")

Advanced query

dataset = NM000305(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000305,
  title = {NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training},
  author = {Marie-Constance Corsi and Christophe Gitton and Juliana Gonzalez-Astudillo and Ari E. Kahn and Laurent Hugueville and Denis Schwartz and Nathalie George and Mario Chavez and Sophie Dupont and Danielle S. Bassett and Fabrizio De Vico Fallani},
  doi = {10.82901/nemar.nm000305},
  url = {https://doi.org/10.82901/nemar.nm000305},
}

§ 02Study · The README

About This Dataset#

NETBCI: longitudinal right-hand motor imagery vs rest BCI training [1]_.

Code: NETBCI2026

Paradigm: imagery DOI: 10.1038/s41597-026-08237-5 Subjects: 19 Sessions per subject: 4 Events: right_hand=1, rest=2 Trial interval: [0, 5] s Runs per session: 6 File format: BrainVision (BIDS)

DOI

NETBCI2026

Acquisition

Sampling rate: 250.0 Hz Number of channels: 74 Channel types: eeg=74 Montage: standard_1005

View full README

DOI

NETBCI2026

Acquisition

Sampling rate: 250.0 Hz Number of channels: 74 Channel types: eeg=74 Montage: standard_1005 Hardware: Easycap 74-channel passive Ag/AgCl EEG amplified by the MEGIN TRIUX MEG acquisition system (102 magnetometers, 204 gradiometers) Software: BCI2000 Reference: mastoids Ground: left scapula Line frequency: 50.0 Hz Impedance threshold: 20.0 kOhm Cap manufacturer: Easycap Electrode type: passive Electrode material: Ag/AgCl

Participants

Number of subjects: 19 Health status: healthy Age: mean=27.47, std=4.07, min=19.0, max=35.0 Gender distribution: female=7, male=12 Handedness: right-handed Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: right_hand, rest Trial duration: 6.0 s Study design: Longitudinal BCI training: 4 sessions on 4 days. One-dimensional two-target box task; up target = sustained right-hand grasping motor imagery, down target = rest. Trial: 1 s ISI then 5 s target presentation with cursor feedback from 3 to 6 s. 6 feedback runs per session, 32 trials per run in the protocol. Feedback type: visual cursor Stimulus type: visual target Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser right_hand

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

rest

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: right_hand, rest Cue duration: 5.0 s Imagery duration: 5.0 s

Data Structure

Trials: 14431 Trials per class: right_hand=7224, rest=7207 Trials context: 6 online feedback runs per session x 4 sessions; the protocol has 32 trials per run (16 per class); the released events hold the authors’ checked trials: 375/456 runs keep 32, the rest 29-31 (357-384 trials per class per subject, 14,431 in total).

Cross-Validation

Method: cross-session Evaluation type: within_subject, cross_session

BCI Application

Applications: motor_control, neurofeedback Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Motor Imagery

Documentation

DOI: 10.1038/s41597-026-08237-5 License: CC-BY-4.0 Investigators: Marie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, Denis Schwartz, Nathalie George, Mario Chavez, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani Senior author: Fabrizio De Vico Fallani Institution: Paris Brain Institute (ICM) Country: FR Repository: Recherche Data Gouv Data URL: https://doi.org/10.57745/RBJRC7 Publication year: 2026 Ethics approval: CPP-IDF-VI of Paris, 2016-A00626-45

References

Corsi, M.-C., Gitton, C., Gonzalez-Astudillo, J., Kahn, A. E., Hugueville, L., Schwartz, D., George, N., Chavez, M., Dupont, S., Bassett, D. S., & De Vico Fallani, F. (2026). Understanding Brain-Computer Interfaces training: a longitudinal and multimodal dataset. Scientific Data. https://doi.org/10.1038/s41597-026-08237-5 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000305-blue)](https://doi.org/10.82901/nemar.nm000305) NETBCI2026 ========== NETBCI: longitudinal right-hand motor imagery vs rest BCI training [1]_. Dataset Overview —————-

Code: NETBCI2026 Paradigm: imagery DOI: 10.1038/s41597-026-08237-5 Subjects: 19 Sessions per subject: 4 Events: right_hand=1, rest=2 Trial interval: [0, 5] s Runs per session: 6 File format: BrainVision (BIDS)

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 74 Channel types: eeg=74 Montage: standard_1005 Hardware: Easycap 74-channel passive Ag/AgCl EEG amplified by the MEGIN TRIUX MEG acquisition system (102 magnetometers, 204 gradiometers) Software: BCI2000 Reference: mastoids Ground: left scapula Line frequency: 50.0 Hz Impedance threshold: 20.0 kOhm Cap manufacturer: Easycap Electrode type: passive Electrode material: Ag/AgCl

Participants#

Number of subjects: 19 Health status: healthy Age: mean=27.47, std=4.07, min=19.0, max=35.0 Gender distribution: female=7, male=12 Handedness: right-handed Species: human

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: right_hand, rest Trial duration: 6.0 s Study design: Longitudinal BCI training: 4 sessions on 4 days. One-dimensional two-target box task; up target = sustained right-hand grasping motor imagery, down target = rest. Trial: 1 s ISI then 5 s target presentation with cursor feedback from 3 to 6 s. 6 feedback runs per session, 32 trials per run in the protocol. Feedback type: visual cursor Stimulus type: visual target Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

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

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: right_hand, rest Cue duration: 5.0 s Imagery duration: 5.0 s

Data Structure#

Trials: 14431 Trials per class: right_hand=7224, rest=7207 Trials context: 6 online feedback runs per session x 4 sessions; the protocol has 32 trials per run (16 per class); the released events hold the authors’ checked trials: 375/456 runs keep 32, the rest 29-31 (357-384 trials per class per subject, 14,431 in total).

Cross-Validation#

Method: cross-session Evaluation type: within_subject, cross_session

BCI Application#

Applications: motor_control, neurofeedback Environment: laboratory Online feedback: True

Tags#

Pathology: Healthy Modality: Motor Type: Motor Imagery

Documentation#

DOI: 10.1038/s41597-026-08237-5 License: CC-BY-4.0 Investigators: Marie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, Denis Schwartz, Nathalie George, Mario Chavez, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani Senior author: Fabrizio De Vico Fallani Institution: Paris Brain Institute (ICM) Country: FR Repository: Recherche Data Gouv Data URL: https://doi.org/10.57745/RBJRC7 Publication year: 2026 Ethics approval: CPP-IDF-VI of Paris, 2016-A00626-45

References#

Corsi, M.-C., Gitton, C., Gonzalez-Astudillo, J., Kahn, A. E., Hugueville, L., Schwartz, D., George, N., Chavez, M., Dupont, S., Bassett, D. S., & De Vico Fallani, F. (2026). Understanding Brain-Computer Interfaces training: a longitudinal and multimodal dataset. Scientific Data. https://doi.org/10.1038/s41597-026-08237-5 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

License: CC-BY-4.0

Authors:

Marie-Constance Corsi
Christophe Gitton
Juliana Gonzalez-Astudillo
Ari E. Kahn
Laurent Hugueville
… and 6 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000305

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=19, range 27–27 yr, mean 27.0 yr)

25

Other · 19

Channel counts: 74 ch (n=456 recordings)

Sampling frequencies: 250.0 Hz (n=456 recordings)

Total recording duration: 29 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 74 ch · EEG · 250 Hz · 28 subjects, 912 recordings

Live trace viewer — sub-9 · ses-04 · task-imagery · run-01

Showing one representative recording out of 28 subjects and 912 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 72 sensors — 72 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000305(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000305

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000305(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training

Study:: nm000305 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000305.

Modality: eeg; Subject type: Unknown. Subjects: 28; recordings: 912; tasks: 2.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000305 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000305 DOI: https://doi.org/10.82901/nemar.nm000305

Examples

>>> from eegdash.dataset import NM000305
>>> dataset = NM000305(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000305.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000305 to reproduce the tutorial on this dataset.

Citation

Marie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, … (2026). NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training. 10.82901/nemar.nm000305

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000305.

Related & sibling datasets

NM000324EEG · 32 subj NM000296EEG · 30 subj NM000325EEG · 15 subj NM000294EEG · 12 subj NM000312EEG · 27 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000305

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000305`
Title	NETBCI2026: NETBCI: longitudinal right-hand motor imagery vs rest BCI training
Author (year)	—
Canonical	—
Importable as	`NM000305`
Year	2026
Authors	Marie-Constance Corsi, Christophe Gitton, Juliana Gonzalez-Astudillo, Ari E. Kahn, Laurent Hugueville, Denis Schwartz, Nathalie George, Mario Chavez, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000305
Source links	OpenNeuro \| NeMAR \| Source URL

NM000305: eeg dataset, 28 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Cross-Validation#

BCI Application#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#