EEGdash›NeMAR›NM000309

Iss. 309 · 18 subjects · 27 recordings · CC-BY-4.0

Dataset Brief · PardoGarcia2026

NM000309: eeg dataset, 18 subjects#

PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls

Access recordings and metadata through EEGDash.

Citation: Rebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo (2026). PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls. 10.82901/nemar.nm000309

Modality: eeg Subjects: 18 Recordings: 27 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

18-participant EEG dataset — PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls.

Data & curation Rebeca Pardo-Garcia · Maria Ruiz-Izquierdo · Mercedes Garcia de la Vega · Rocio Calvillo · George Kontaxakis · Eva M. Moreno · …
Senior author M. A. Pozo
Year 2026 · Distributed via NeMAR

EEG · 59 ch1000 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 NM000309

dataset = NM000309(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 = NM000309(cache_dir="./data", subject="01")

Advanced query

dataset = NM000309(
    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{nm000309,
  title = {PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls},
  author = {Rebeca Pardo-Garcia and Maria Ruiz-Izquierdo and Mercedes Garcia de la Vega and Rocio Calvillo and George Kontaxakis and Eva M. Moreno and M. A. Pozo},
  doi = {10.82901/nemar.nm000309},
  url = {https://doi.org/10.82901/nemar.nm000309},
}

§ 02Study · The README

About This Dataset#

Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls [1]_.

Code: PardoGarcia2026

Paradigm: imagery DOI: 10.5281/zenodo.19599465 Subjects: 18 Sessions per subject: 1 (loader default declares 1 per MOABB minimum-sessions convention; deposited tree holds ses-0pre and ses-1post for 9 chronic MCA stroke patients (sub-1, sub-3..sub-10) and ses-0pre only for the remaining 9 subjects: sub-2 lost to follow-up and sub-11..sub-18 healthy controls; 27 sessions total) Events: pinch=1, fist=2 Trial interval: [0, 1.5] s

DOI

PardoGarcia2026

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 63 Channel types: eeg=59, eog=4 Channel names: O2, OZ, O1, PO8, PO6, PO4, POZ, PO3, PO5, PO7, P8, P6, P4, P2, PZ, P1, P3, P5, P7, TP8, CP6, CP4, CP2, CPZ, CP1, CP3, CP5, TP7, HEOGn, HEOGp, VEOGn, VEOGp, FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, T7, C5, C3, C1, CZ, C2, C4, C6, T8, A1

View full README

DOI

PardoGarcia2026

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 63 Channel types: eeg=59, eog=4 Channel names: O2, OZ, O1, PO8, PO6, PO4, POZ, PO3, PO5, PO7, P8, P6, P4, P2, PZ, P1, P3, P5, P7, TP8, CP6, CP4, CP2, CPZ, CP1, CP3, CP5, TP7, HEOGn, HEOGp, VEOGn, VEOGp, FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, T7, C5, C3, C1, CZ, C2, C4, C6, T8, A1 Montage: 10-10 Hardware: BrainVision (Brain Products GmbH) Reference: A2 (right mastoid) Sensor type: Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Electro-Cap International

Participants

Number of subjects: 18 Health status: mixed Clinical population: chronic middle cerebral artery (MCA) stroke

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: pinch, fist Trial duration: 1.5 s Study design: Image-cued two-class hand motor imagery and preparation (precision pinch vs closed fist) before a later auditory go cue and overt execution. The adapter exposes only the pre-execution phase. Feedback type: none Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline Instructions: Imagine and prepare the hand grip shown in the cue image (a precision pinch or a closed fist); overt execution begins only after the later auditory go cue, outside the exposed interval.

HED Event Annotations

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

     ├─ Sensory-event
     └─ Label/pinch

fist

├─ Sensory-event
└─ Label/fist

Tags

Pathology: stroke, healthy Modality: Motor Type: Motor Imagery

Documentation

Description: Pre-execution, two-class hand motor-imagery EEG (pinch vs fist) from recordings that continue into cued overt execution: 10 chronic MCA stroke patients (baseline and post-rehabilitation) and 8 healthy controls, 63 BrainVision channels at 1000 Hz. DOI: 10.5281/zenodo.19599465 License: CC-BY-4.0 Investigators: Rebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo Institution: Instituto Pluridisciplinar, Universidad Complutense de Madrid; Universidad Politecnica de Madrid; Hospital Clinico San Carlos Country: ES Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.19599465 Publication year: 2026 Keywords: EEG, motor imagery, motor preparation, motor execution, chronic stroke, MCA stroke, rehabilitation, mu rhythm, beta rhythm, ERD

References

Pardo-Garcia, R., Ruiz-Izquierdo, M., Garcia de la Vega, M., Calvillo, R., Kontaxakis, G., Moreno, E. M., and Pozo, M. A. (2026). Mu and Beta Oscillatory Changes during a motor task following Rehabilitation in Chronic MCA Stroke: Insights from EEG. Zenodo. DOI: https://doi.org/10.5281/zenodo.19599465 Notes .. versionadded:: 1.8.0 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.

Ethics

Approved by the Medical Ethical Reviewing Committee of the Hospital Clínico San Carlos (Madrid, Spain), approval number 22/459-E_Tesis. The study was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Patients provided written informed consent; healthy controls provided verbal informed consent (Pardo-Garcia et al. 2026, preprint DOI 10.21203/rs.3.rs-6958817/v1).

Verbatim from the source:

This study was approved by the Medical Ethical Reviewing Committee of the Hospital Clínico San Carlos in Madrid, Spain (assigned number Nº22/459-E_Tesis) and was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).

Source: cached paper .paper-audit/PardoGarcia2026/paper-10_21203_rs_3_rs_6958817_v1.txt (Pardo-Garcia et al. 2026, Research Square preprint, DOI 10.21203/rs.3.rs-6958817/v1).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000309-blue)](https://doi.org/10.82901/nemar.nm000309) PardoGarcia2026 =============== Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls [1]_. Dataset Overview —————-

Code: PardoGarcia2026 Paradigm: imagery DOI: 10.5281/zenodo.19599465 Subjects: 18 Sessions per subject: 1 (loader default declares 1 per MOABB minimum-sessions convention; deposited tree holds ses-0pre and ses-1post for 9 chronic MCA stroke patients (sub-1, sub-3..sub-10) and ses-0pre only for the remaining 9 subjects: sub-2 lost to follow-up and sub-11..sub-18 healthy controls; 27 sessions total) Events: pinch=1, fist=2 Trial interval: [0, 1.5] s

Acquisition#

Sampling rate: 1000.0 Hz Number of channels: 63 Channel types: eeg=59, eog=4 Channel names: O2, OZ, O1, PO8, PO6, PO4, POZ, PO3, PO5, PO7, P8, P6, P4, P2, PZ, P1, P3, P5, P7, TP8, CP6, CP4, CP2, CPZ, CP1, CP3, CP5, TP7, HEOGn, HEOGp, VEOGn, VEOGp, FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, T7, C5, C3, C1, CZ, C2, C4, C6, T8, A1 Montage: 10-10 Hardware: BrainVision (Brain Products GmbH) Reference: A2 (right mastoid) Sensor type: Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Electro-Cap International

Participants#

Number of subjects: 18 Health status: mixed Clinical population: chronic middle cerebral artery (MCA) stroke

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: pinch, fist Trial duration: 1.5 s Study design: Image-cued two-class hand motor imagery and preparation (precision pinch vs closed fist) before a later auditory go cue and overt execution. The adapter exposes only the pre-execution phase. Feedback type: none Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline Instructions: Imagine and prepare the hand grip shown in the cue image (a precision pinch or a closed fist); overt execution begins only after the later auditory go cue, outside the exposed interval.

HED Event Annotations#

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

├─ Sensory-event └─ Label/pinch

fist
├─ Sensory-event └─ Label/fist

Tags#

Pathology: stroke, healthy Modality: Motor Type: Motor Imagery

Documentation#

Description: Pre-execution, two-class hand motor-imagery EEG (pinch vs fist) from recordings that continue into cued overt execution: 10 chronic MCA stroke patients (baseline and post-rehabilitation) and 8 healthy controls, 63 BrainVision channels at 1000 Hz. DOI: 10.5281/zenodo.19599465 License: CC-BY-4.0 Investigators: Rebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo Institution: Instituto Pluridisciplinar, Universidad Complutense de Madrid; Universidad Politecnica de Madrid; Hospital Clinico San Carlos Country: ES Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.19599465 Publication year: 2026 Keywords: EEG, motor imagery, motor preparation, motor execution, chronic stroke, MCA stroke, rehabilitation, mu rhythm, beta rhythm, ERD

References#

Pardo-Garcia, R., Ruiz-Izquierdo, M., Garcia de la Vega, M., Calvillo, R., Kontaxakis, G., Moreno, E. M., and Pozo, M. A. (2026). Mu and Beta Oscillatory Changes during a motor task following Rehabilitation in Chronic MCA Stroke: Insights from EEG. Zenodo. DOI: https://doi.org/10.5281/zenodo.19599465 Notes .. versionadded:: 1.8.0 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. ## Ethics Approved by the Medical Ethical Reviewing Committee of the Hospital Clínico San Carlos (Madrid, Spain), approval number 22/459-E_Tesis. The study was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Patients provided written informed consent; healthy controls provided verbal informed consent (Pardo-Garcia et al. 2026, preprint DOI 10.21203/rs.3.rs-6958817/v1). Verbatim from the source: > This study was approved by the Medical Ethical Reviewing Committee of the Hospital Clínico San Carlos in Madrid, Spain (assigned number Nº22/459-E_Tesis) and was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Source: cached paper .paper-audit/PardoGarcia2026/paper-10_21203_rs_3_rs_6958817_v1.txt (Pardo-Garcia et al. 2026, Research Square preprint, DOI 10.21203/rs.3.rs-6958817/v1).

License: CC-BY-4.0

Authors:

Rebeca Pardo-Garcia
Maria Ruiz-Izquierdo
Mercedes Garcia de la Vega
Rocio Calvillo
George Kontaxakis
… and 2 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000309

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 59 ch (n=27 recordings)

Sampling frequencies: 1000.0 Hz (n=27 recordings)

Total recording duration: 12 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 59 ch · EEG · 1000 Hz · 18 subjects, 27 recordings

Live trace viewer — sub-7 · ses-1post · task-imagery · run-0

Showing one representative recording out of 18 subjects and 27 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.NM000309(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000309

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

class eegdash.dataset.NM000309(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls

Study:: nm000309 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000309.

Modality: eeg; Subject type: Unknown. Subjects: 18; recordings: 27; 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/nm000309 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000309 DOI: https://doi.org/10.82901/nemar.nm000309

Examples

>>> from eegdash.dataset import NM000309
>>> dataset = NM000309(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 — NM000309.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 nm000309 to reproduce the tutorial on this dataset.

Citation

Rebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, … (2026). PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls. 10.82901/nemar.nm000309

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000309.

Related & sibling datasets

NM000316EEG · 5 subj NM000330EEG · 22 subj NM000295EEG · 29 subj NM000150EEG NM000306EEG · 15 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.nm000309

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000309`
Title	PardoGarcia2026: Mu/beta motor-imagery EEG in chronic MCA stroke and healthy controls
Author (year)	—
Canonical	—
Importable as	`NM000309`
Year	2026
Authors	Rebeca Pardo-Garcia, Maria Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000309
Source links	OpenNeuro \| NeMAR \| Source URL

NM000309: eeg dataset, 18 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#