EEGdash›NeMAR›NM000299

Iss. 299 · 8 subjects · 720 recordings · CC0-1.0

Dataset Brief · WRCC2023_MI_C

NM000299: eeg dataset, 8 subjects#

WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C)

Access recordings and metadata through EEGDash.

Citation: WRCC2023 (2024). WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C). 10.82901/nemar.nm000299

Modality: eeg Subjects: 8 Recordings: 720 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

8-participant EEG dataset — WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C).

Data & curation WRCC2023
Senior author WRCC2023
Year 2024 · Distributed via NeMAR

EEG · 59 ch1000 HzBIDS 1.9.0Task · imagery

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 NM000299

dataset = NM000299(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 = NM000299(cache_dir="./data", subject="01")

Advanced query

dataset = NM000299(
    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{nm000299,
  title = {WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C)},
  author = {WRCC2023},
  doi = {10.82901/nemar.nm000299},
  url = {https://doi.org/10.82901/nemar.nm000299},
}

§ 02Study · The README

About This Dataset#

Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C).

Code: WRCC2023-MI-C

Paradigm: imagery DOI: 10.7910/DVN/G8FBHH Subjects: 8 Sessions per subject: 1 Events: left_hand=1, right_hand=2, feet=3 Trial interval: [0, 3.999] s Runs per session: 90 File format: mat

DOI

WRCC2023-MI-C

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 59 Channel types: eeg=59 Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2

View full README

DOI

WRCC2023-MI-C

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 59 Channel types: eeg=59 Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2 Montage: standard_1005 Hardware: Neuracle NeuSen W 64-channel wireless EEG (channels 1-59 EEG) Line frequency: 50.0 Hz

Participants

Number of subjects: 8 Health status: mixed: healthy individuals and stroke patients (2 stroke) Clinical population: stroke

Experimental Protocol

Paradigm: imagery Number of classes: 3 Class labels: left_hand, right_hand, feet Trials per class: left_hand=30, right_hand=30, feet=30 Synchronicity: cue-based Mode: offline

HED Event Annotations

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

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

right_hand

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

feet

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

Tags

Modality: Motor Type: Motor Imagery

Documentation

Description: Three-class (left hand, right hand, feet) motor imagery EEG dataset from the 2023 World Robot Contest BCI competition (MI-C track); 8 subjects including 2 stroke patients, 90 trials each. DOI: 10.7910/DVN/G8FBHH License: CC0-1.0 Investigators: WRCC2023 Institution: World Robot Contest (BCI-Controlled Robot Contest) Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/G8FBHH Publication year: 2024

References

WRCC2023 (2024). MI-C dataset of the BCI competition WRCC2023. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/G8FBHH Notes Each stored trial is exposed as a separate run to avoid filtering across discontinuities. The inclusive epoch endpoint is 3.999 s (4000 samples), without synthetic zero padding. Channel order and physical calibration follow the reconciled source loaders and still require source verification.

The Harvard Dataverse record (V1, released 2024-07-05, CC0 1.0) lists eight files subject1.mat..``subject8.mat``; its description only states that two of the individuals are stroke patients. No paper is linked (paper audit, 2026-09-30). .. versionadded:: 1.8 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

Ethics

Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.

Please consult the primary publication for the exact IRB/ethics committee reference.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000299-blue)](https://doi.org/10.82901/nemar.nm000299) WRCC2023-MI-C ============= Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C). Dataset Overview —————-

Code: WRCC2023-MI-C Paradigm: imagery DOI: 10.7910/DVN/G8FBHH Subjects: 8 Sessions per subject: 1 Events: left_hand=1, right_hand=2, feet=3 Trial interval: [0, 3.999] s Runs per session: 90 File format: mat

Acquisition#

Sampling rate: 1000.0 Hz Number of channels: 59 Channel types: eeg=59 Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO5, PO6, PO7, PO8, Oz, O1, O2 Montage: standard_1005 Hardware: Neuracle NeuSen W 64-channel wireless EEG (channels 1-59 EEG) Line frequency: 50.0 Hz

Participants#

Number of subjects: 8 Health status: mixed: healthy individuals and stroke patients (2 stroke) Clinical population: stroke

Experimental Protocol#

Paradigm: imagery Number of classes: 3 Class labels: left_hand, right_hand, feet Trials per class: left_hand=30, right_hand=30, feet=30 Synchronicity: cue-based Mode: offline

HED Event Annotations#

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

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

└─ Imagine
├─ Move └─ Left, Hand

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

└─ Imagine
├─ Move └─ Right, Hand

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

└─ Imagine, Move, Foot

Tags#

Modality: Motor Type: Motor Imagery

Documentation#

Description: Three-class (left hand, right hand, feet) motor imagery EEG dataset from the 2023 World Robot Contest BCI competition (MI-C track); 8 subjects including 2 stroke patients, 90 trials each. DOI: 10.7910/DVN/G8FBHH License: CC0-1.0 Investigators: WRCC2023 Institution: World Robot Contest (BCI-Controlled Robot Contest) Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/G8FBHH Publication year: 2024

References#

WRCC2023 (2024). MI-C dataset of the BCI competition WRCC2023. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/G8FBHH Notes Each stored trial is exposed as a separate run to avoid filtering across discontinuities. The inclusive epoch endpoint is 3.999 s (4000 samples), without synthetic zero padding. Channel order and physical calibration follow the reconciled source loaders and still require source verification. The Harvard Dataverse record (V1, released 2024-07-05, CC0 1.0) lists eight files subject1.mat..``subject8.mat``; its description only states that two of the individuals are stroke patients. No paper is linked (paper audit, 2026-09-30). .. versionadded:: 1.8 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 Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference.

License: CC0-1.0

Authors:

WRCC2023

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000299

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 59 ch (n=720 recordings)

Sampling frequencies: 1000.0 Hz (n=720 recordings)

Total recording duration: 47 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 59 ch · EEG · 1000 Hz · 8 subjects, 720 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 8 subjects and 720 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 · 59 sensors — 59 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.NM000299(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000299

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

class eegdash.dataset.NM000299(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C)

Study:: nm000299 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000299.

Modality: eeg; Subject type: Unknown. Subjects: 8; recordings: 720; tasks: 1.

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/nm000299 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000299 DOI: https://doi.org/10.82901/nemar.nm000299

Examples

>>> from eegdash.dataset import NM000299
>>> dataset = NM000299(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 — NM000299.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 nm000299 to reproduce the tutorial on this dataset.

Citation

WRCC2023 (2024). WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C). 10.82901/nemar.nm000299

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000299.

Related & sibling datasets

NM000297EEG · 9 subj NM000298EEG · 9 subj NM000316EEG · 5 subj NM000309EEG · 18 subj NM000330EEG · 22 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000299

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000299`
Title	WRCC2023_MI_C: Three-class motor imagery dataset from the World Robot Contest 2023 (MI-C)
Author (year)	—
Canonical	—
Importable as	`NM000299`
Year	2024
Authors	WRCC2023
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000299
Source links	OpenNeuro \| NeMAR \| Source URL

NM000299: eeg dataset, 8 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

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#