EEGdash›NeMAR›NM000312

Iss. 312 · 27 subjects · 113 recordings · CC-BY-4.0

Dataset Brief · Alawieh2025

NM000312: eeg dataset, 27 subjects#

Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation

Access recordings and metadata through EEGDash.

Citation: Hussein Alawieh, Deland Liu, Jonathan Madera, Satyam Kumar, Frigyes Samuel Racz, Ann Majewicz Fey, José del R. Millán (2025). Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation. 10.82901/nemar.nm000312

Modality: eeg Subjects: 27 Recordings: 113 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

27-participant EEG dataset — Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation.

Data & curation Hussein Alawieh · Deland Liu · Jonathan Madera · Satyam Kumar · Frigyes Samuel Racz · Ann Majewicz Fey · …
Senior author José del R. Millán
Year 2025 · Distributed via NeMAR

EEG · 32 ch512 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 NM000312

dataset = NM000312(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 = NM000312(cache_dir="./data", subject="01")

Advanced query

dataset = NM000312(
    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{nm000312,
  title = {Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation},
  author = {Hussein Alawieh and Deland Liu and Jonathan Madera and Satyam Kumar and Frigyes Samuel Racz and Ann Majewicz Fey and José del R. Millán},
  doi = {10.82901/nemar.nm000312},
  url = {https://doi.org/10.82901/nemar.nm000312},
}

§ 02Study · The README

About This Dataset#

Motor-imagery BCI dataset with transcutaneous spinal stimulation [1]_ [2]_.

Code: Alawieh2025

Paradigm: imagery DOI: 10.5281/zenodo.15454354 Subjects: 27 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: (0, 4) s File format: GDF

DOI

Alawieh2025

Acquisition

Sampling rate: 512.0 Hz Number of channels: 35 Channel types: eeg=32, eog=3 Channel names: FP1, FPZ, FP2, F7, F3, FZ, F4, F8, FC5, FC1, FC2, FC6, M1, T7, C3, CZ, C4, T8, M2, CP5, CP1, CP2, CP6, P7, P3, PZ, P4, P8, POZ, O1, OZ, O2, sens7, sens8, sens9

View full README

DOI

Alawieh2025

Acquisition

Sampling rate: 512.0 Hz Number of channels: 35 Channel types: eeg=32, eog=3 Channel names: FP1, FPZ, FP2, F7, F3, FZ, F4, F8, FC5, FC1, FC2, FC6, M1, T7, C3, CZ, C4, T8, M2, CP5, CP1, CP2, CP6, P7, P3, PZ, P4, P8, POZ, O1, OZ, O2, sens7, sens8, sens9 Montage: 10-20 Hardware: ANT Neuro eego with Ag/AgCl-coated electrodes Reference: CPz Ground: AFz Sensor type: Ag/AgCl Line frequency: 50.0 Hz Auxiliary channels: EOG (3 ch)

Participants

Number of subjects: 27 Health status: able-bodied and spinal cord injury Clinical population: 25 able-bodied, 2 spinal cord injury (SCI)

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Feedback type: kinesthetic 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

Tags

Pathology: healthy, spinal cord injury Modality: Motor Type: Motor Imagery

Documentation

Description: Longitudinal two-class (left/right hand) motor-imagery BCI training dataset with transcutaneous electrical spinal stimulation, in able-bodied and spinal cord injury participants. DOI: 10.5281/zenodo.15454354 Associated paper DOI: 10.1073/pnas.2418920122 License: CC-BY-4.0 Investigators: Hussein Alawieh, Deland Liu, Jonathan Madera, Satyam Kumar, Frigyes Samuel Racz, Ann Majewicz Fey, José del R. Millán Institution: The University of Texas at Austin Country: US Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.15454354 Publication year: 2025 Ethics approval: University of Texas at Austin IRB Protocol #2020-03-0073; ClinicalTrials.gov NCT05183152

References

Alawieh, H., Liu, D., Madera, J., Kumar, S., Racz, F. S., Fey, A. M., & Millán, J. del R. (2025). Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning. Proceedings of the National Academy of Sciences, 122(24). DOI: https://doi.org/10.1073/pnas.2418920122 Alawieh, H., Liu, D., Madera, J., Kumar, S., Racz, F. S., Majewicz Fey, A., & Millán, J. del R. (2025). A Multi-Session EEG Dataset of Longitudinal Motor Imagery BCI Training with Transcutaneous Spinal Stimulation in Able-Bodied and Spinal Cord Injury Participants. Zenodo. DOI: https://doi.org/10.5281/zenodo.15454354 .. 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

Approved by the University of Texas at Austin Institutional Review Board (IRB protocol number 2020-03-0073); the study protocol is registered on ClinicalTrials.gov as NCT05183152. Written informed consent was obtained from all participants (Alawieh et al. 2025, PNAS, DOI 10.1073/pnas.2418920122).

Verbatim from the source:

The study recruited 25 healthy individuals and two SCI patients who provided written informed consent to the procedures of the study protocol, which is published on ClinicalTrials.gov (NCT05183152), as approved by the University of Texas at Austin Institutional Review Board (IRB protocol number: 2020-03-0073).

Source: cached paper .paper-audit/Alawieh2025/paper-10_1073_pnas_2418920122.txt (Alawieh et al. 2025, PNAS 122, DOI 10.1073/pnas.2418920122).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000312-blue)](https://doi.org/10.82901/nemar.nm000312) Alawieh2025 =========== Motor-imagery BCI dataset with transcutaneous spinal stimulation [1]_ [2]_. Dataset Overview —————-

Code: Alawieh2025 Paradigm: imagery DOI: 10.5281/zenodo.15454354 Subjects: 27 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: (0, 4) s File format: GDF

Acquisition#

Sampling rate: 512.0 Hz Number of channels: 35 Channel types: eeg=32, eog=3 Channel names: FP1, FPZ, FP2, F7, F3, FZ, F4, F8, FC5, FC1, FC2, FC6, M1, T7, C3, CZ, C4, T8, M2, CP5, CP1, CP2, CP6, P7, P3, PZ, P4, P8, POZ, O1, OZ, O2, sens7, sens8, sens9 Montage: 10-20 Hardware: ANT Neuro eego with Ag/AgCl-coated electrodes Reference: CPz Ground: AFz Sensor type: Ag/AgCl Line frequency: 50.0 Hz Auxiliary channels: EOG (3 ch)

Participants#

Number of subjects: 27 Health status: able-bodied and spinal cord injury Clinical population: 25 able-bodied, 2 spinal cord injury (SCI)

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Feedback type: kinesthetic 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

Tags#

Pathology: healthy, spinal cord injury Modality: Motor Type: Motor Imagery

Documentation#

Description: Longitudinal two-class (left/right hand) motor-imagery BCI training dataset with transcutaneous electrical spinal stimulation, in able-bodied and spinal cord injury participants. DOI: 10.5281/zenodo.15454354 Associated paper DOI: 10.1073/pnas.2418920122 License: CC-BY-4.0 Investigators: Hussein Alawieh, Deland Liu, Jonathan Madera, Satyam Kumar, Frigyes Samuel Racz, Ann Majewicz Fey, José del R. Millán Institution: The University of Texas at Austin Country: US Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.15454354 Publication year: 2025 Ethics approval: University of Texas at Austin IRB Protocol #2020-03-0073; ClinicalTrials.gov NCT05183152

References#

Alawieh, H., Liu, D., Madera, J., Kumar, S., Racz, F. S., Fey, A. M., & Millán, J. del R. (2025). Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning. Proceedings of the National Academy of Sciences, 122(24). DOI: https://doi.org/10.1073/pnas.2418920122 Alawieh, H., Liu, D., Madera, J., Kumar, S., Racz, F. S., Majewicz Fey, A., & Millán, J. del R. (2025). A Multi-Session EEG Dataset of Longitudinal Motor Imagery BCI Training with Transcutaneous Spinal Stimulation in Able-Bodied and Spinal Cord Injury Participants. Zenodo. DOI: https://doi.org/10.5281/zenodo.15454354 .. 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 Approved by the University of Texas at Austin Institutional Review Board (IRB protocol number 2020-03-0073); the study protocol is registered on ClinicalTrials.gov as NCT05183152. Written informed consent was obtained from all participants (Alawieh et al. 2025, PNAS, DOI 10.1073/pnas.2418920122). Verbatim from the source: > The study recruited 25 healthy individuals and two SCI patients who provided written informed consent to the procedures of the study protocol, which is published on ClinicalTrials.gov (NCT05183152), as approved by the University of Texas at Austin Institutional Review Board (IRB protocol number: 2020-03-0073). Source: cached paper .paper-audit/Alawieh2025/paper-10_1073_pnas_2418920122.txt (Alawieh et al. 2025, PNAS 122, DOI 10.1073/pnas.2418920122).

License: CC-BY-4.0

Authors:

Hussein Alawieh
Deland Liu
Jonathan Madera
Satyam Kumar
Frigyes Samuel Racz
… and 2 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000312

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=27, range 2020–2022 yr)

2020

Other · 27

Channel counts: 32 ch (n=113 recordings)

Sampling frequencies: 512.0 Hz (n=113 recordings)

Total recording duration: 8 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 512 Hz · 27 subjects, 113 recordings

Live trace viewer — sub-7 · ses-0 · task-imagery · run-3

Showing one representative recording out of 27 subjects and 113 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 · 32 sensors — 32 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.NM000312(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000312

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

class eegdash.dataset.NM000312(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation

Study:: nm000312 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000312.

Modality: eeg; Subject type: Unknown. Subjects: 27; recordings: 113; 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/nm000312 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000312 DOI: https://doi.org/10.82901/nemar.nm000312

Examples

>>> from eegdash.dataset import NM000312
>>> dataset = NM000312(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 — NM000312.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 nm000312 to reproduce the tutorial on this dataset.

Citation

Hussein Alawieh, Deland Liu, Jonathan Madera, Satyam Kumar, Frigyes Samuel Racz, … (2025). Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation. 10.82901/nemar.nm000312

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000312.

Related & sibling datasets

NM000318EEG · 20 subj NM000306EEG · 15 subj NM000293EEG · 11 subj NM000385EEG · 14 subj NM000319EEG · 8 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.nm000312

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000312`
Title	Alawieh2025: Motor-imagery BCI dataset with transcutaneous spinal stimulation
Author (year)	—
Canonical	—
Importable as	`NM000312`
Year	2025
Authors	Hussein Alawieh, Deland Liu, Jonathan Madera, Satyam Kumar, Frigyes Samuel Racz, Ann Majewicz Fey, José del R. Millán
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000312
Source links	OpenNeuro \| NeMAR \| Source URL

NM000312: eeg dataset, 27 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#