EEGdash›NeMAR›NM000302

Iss. 302 · 6 subjects · 132 recordings · CC-BY-4.0

Dataset Brief · PoloHortiguela2025

NM000302: eeg dataset, 6 subjects#

PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset

Access recordings and metadata through EEGDash.

Citation: Cristina Polo-Hortiguela, Mario Ortiz, Eduardo Ianez, Jose M. Azorin (2025). PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset. 10.82901/nemar.nm000302

Modality: eeg Subjects: 6 Recordings: 132 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

6-participant EEG dataset — PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset.

Data & curation Cristina Polo-Hortiguela · Mario Ortiz · Eduardo Ianez · Jose M. Azorin
Senior author Jose M. Azorin
Year 2025 · Distributed via NeMAR
Funding PID2021-124111OB-C31 (MICIU/AEI/10.13039/501100011033, ERDF EU) · PRE2022-103336 (MICIU/AEI/10.13039/501100011033) · + 2 more

EEG · 28 ch250 HzBIDS 1.9.0Task · imagery2 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 NM000302

dataset = NM000302(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 = NM000302(cache_dir="./data", subject="01")

Advanced query

dataset = NM000302(
    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{nm000302,
  title = {PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset},
  author = {Cristina Polo-Hortiguela and Mario Ortiz and Eduardo Ianez and Jose M. Azorin},
  doi = {10.82901/nemar.nm000302},
  url = {https://doi.org/10.82901/nemar.nm000302},
}

§ 02Study · The README

About This Dataset#

Motor imagery of ankle dorsiflexion/plantarflexion dataset [1]_.

Code: PoloHortiguela2025

Paradigm: imagery DOI: 10.5281/zenodo.14672334 Subjects: 6 Sessions per subject: 2 Events: rest=1, motor_imagery=2 Trial interval: [0, 4] s Runs per session: 11 Session IDs: 0static, 1motion File format: MAT

DOI

PoloHortiguela2025

Acquisition

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=28, eog=4, misc=3 Channel names: AF3, F3, Fz, FC3, FC1, FCz, C5, C3, C1, Cz, CP3, CP1, CPz, P3, Pz, PO3, AF4, F4, FC2, FC4, C2, C4, C6, CP2, CP4, P4, POz, PO4

View full README

DOI

PoloHortiguela2025

Acquisition

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=28, eog=4, misc=3 Channel names: AF3, F3, Fz, FC3, FC1, FCz, C5, C3, C1, Cz, CP3, CP1, CPz, P3, Pz, PO3, AF4, F4, FC2, FC4, C2, C4, C6, CP2, CP4, P4, POz, PO4 Montage: standard_1005 Line frequency: 50.0 Hz Auxiliary channels: EOG (4 ch, vertical, vertical, horizontal, horizontal)

Participants

Number of subjects: 6 Health status: healthy

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: rest, motor_imagery Trial duration: 4.0 s Study design: Kinesthetic motor imagery of ankle dorsiflexion/plantarflexion alternated with relaxation, with a static or motion lower-limb exoskeleton. Stimulus type: auditory Stimulus modalities: audio Synchronicity: cue-based Mode: offline

HED Event Annotations

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

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

motor_imagery

├─ Sensory-event
└─ Label/motor_imagery

Tags

Pathology: healthy Modality: motor Type: imagery

Documentation

Description: Open-loop EEG dataset of lower-limb (ankle dorsiflexion/plantarflexion) kinesthetic motor imagery versus relaxation from six healthy participants, recorded with a static and a motion exoskeleton model. DOI: 10.5281/zenodo.14672334 License: CC-BY-4.0 Investigators: Cristina Polo-Hortiguela, Mario Ortiz, Eduardo Ianez, Jose M. Azorin Institution: Universidad Miguel Hernandez de Elche Country: ES Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.14672334 Publication year: 2025 Funding: PID2021-124111OB-C31 (MICIU/AEI/10.13039/501100011033, ERDF EU); PRE2022-103336 (MICIU/AEI/10.13039/501100011033); ValgrAI (Generalitat Valenciana, European Union); Neurokit (ICAR)

References

Polo-Hortiguela, C., Ortiz, M., Ianez, E., & Azorin, J. M. (2025). EEG Signal Dataset During Dorsiflexion and Plantar Flexion Movements. Zenodo. https://doi.org/10.5281/zenodo.14672334 Notes .. versionadded:: 1.2.1 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.nm000302-blue)](https://doi.org/10.82901/nemar.nm000302) PoloHortiguela2025 ================== Motor imagery of ankle dorsiflexion/plantarflexion dataset [1]_. Dataset Overview —————-

Code: PoloHortiguela2025 Paradigm: imagery DOI: 10.5281/zenodo.14672334 Subjects: 6 Sessions per subject: 2 Events: rest=1, motor_imagery=2 Trial interval: [0, 4] s Runs per session: 11 Session IDs: 0static, 1motion File format: MAT

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 35 Channel types: eeg=28, eog=4, misc=3 Channel names: AF3, F3, Fz, FC3, FC1, FCz, C5, C3, C1, Cz, CP3, CP1, CPz, P3, Pz, PO3, AF4, F4, FC2, FC4, C2, C4, C6, CP2, CP4, P4, POz, PO4 Montage: standard_1005 Line frequency: 50.0 Hz Auxiliary channels: EOG (4 ch, vertical, vertical, horizontal, horizontal)

Participants#

Number of subjects: 6 Health status: healthy

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: rest, motor_imagery Trial duration: 4.0 s Study design: Kinesthetic motor imagery of ankle dorsiflexion/plantarflexion alternated with relaxation, with a static or motion lower-limb exoskeleton. Stimulus type: auditory Stimulus modalities: audio Synchronicity: cue-based Mode: offline

HED Event Annotations#

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

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

motor_imagery
├─ Sensory-event └─ Label/motor_imagery

Tags#

Pathology: healthy Modality: motor Type: imagery

Documentation#

Description: Open-loop EEG dataset of lower-limb (ankle dorsiflexion/plantarflexion) kinesthetic motor imagery versus relaxation from six healthy participants, recorded with a static and a motion exoskeleton model. DOI: 10.5281/zenodo.14672334 License: CC-BY-4.0 Investigators: Cristina Polo-Hortiguela, Mario Ortiz, Eduardo Ianez, Jose M. Azorin Institution: Universidad Miguel Hernandez de Elche Country: ES Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.14672334 Publication year: 2025 Funding: PID2021-124111OB-C31 (MICIU/AEI/10.13039/501100011033, ERDF EU); PRE2022-103336 (MICIU/AEI/10.13039/501100011033); ValgrAI (Generalitat Valenciana, European Union); Neurokit (ICAR)

References#

Polo-Hortiguela, C., Ortiz, M., Ianez, E., & Azorin, J. M. (2025). EEG Signal Dataset During Dorsiflexion and Plantar Flexion Movements. Zenodo. https://doi.org/10.5281/zenodo.14672334 Notes .. versionadded:: 1.2.1 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: CC-BY-4.0

Authors:

Cristina Polo-Hortiguela
Mario Ortiz
Eduardo Ianez
Jose M. Azorin

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000302

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 28 ch (n=132 recordings)

Sampling frequencies: 250.0 Hz (n=132 recordings)

Total recording duration: 3 h 4 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 28 ch · EEG · 250 Hz · 6 subjects, 132 recordings

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

Showing one representative recording out of 6 subjects and 132 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 · 28 sensors — 28 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.NM000302(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000302

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

class eegdash.dataset.NM000302(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset

Study:: nm000302 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000302.

Modality: eeg; Subject type: Unknown. Subjects: 6; recordings: 132; 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/nm000302 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000302 DOI: https://doi.org/10.82901/nemar.nm000302

Examples

>>> from eegdash.dataset import NM000302
>>> dataset = NM000302(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 — NM000302.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 nm000302 to reproduce the tutorial on this dataset.

Citation

Cristina Polo-Hortiguela, Mario Ortiz, Eduardo Ianez, Jose M. Azorin (2025). PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset. 10.82901/nemar.nm000302

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000302.

Related & sibling datasets

NM000315EEG · 10 subj NM000294EEG · 12 subj NM000300EEG · 10 subj NM000320EEG · 10 subj NM000308EEG · 2 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.nm000302

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000302`
Title	PoloHortiguela2025: Motor imagery of ankle dorsiflexion/plantarflexion dataset
Author (year)	—
Canonical	—
Importable as	`NM000302`
Year	2025
Authors	Cristina Polo-Hortiguela, Mario Ortiz, Eduardo Ianez, Jose M. Azorin
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000302
Source links	OpenNeuro \| NeMAR \| Source URL

NM000302: eeg dataset, 6 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#