NM000304: eeg dataset, 54 subjects#
PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset
Access recordings and metadata through EEGDash.
Citation: Jorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca (2026). PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset. 10.82901/nemar.nm000304
Modality: eeg Subjects: 54 Recordings: 448 License: CC-BY-4.0 Source: nemar
Metadata: Complete (100%)
54-participant EEG dataset — PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000304
dataset = NM000304(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000304(cache_dir="./data", subject="01")
Advanced query
dataset = NM000304(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{nm000304,
title = {PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset},
author = {Jorge German Perez Blanco and Javier Mauricio Antelis Ortiz and Luis Guillermo Hernandez Rojas and Hector Lizarraga Torreblanca},
doi = {10.82901/nemar.nm000304},
url = {https://doi.org/10.82901/nemar.nm000304},
}
About This Dataset#
Wrist-motion (4-direction pointing) motor-execution dataset [1]_.
Code: PerezBlanco2026
Paradigm: imagery DOI: 10.1038/s41597-026-07287-z Subjects: 45 Sessions per subject: 1 Events: flexion=1, extension=2, radial_deviation=3, ulnar_deviation=4 Trial interval: [0, 2.5] s Runs per session: 8 File format: EDF (BIDS, converted from BCI2000 .dat)
PerezBlanco2026
Acquisition
Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=8, emg=8 Channel names: C3, C1, Cz, C2, C4, CP3, CPz, CP4
View full README
PerezBlanco2026
Acquisition
Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=8, emg=8 Channel names: C3, C1, Cz, C2, C4, CP3, CPz, CP4 Montage: 10-10 Hardware: g.tec g.USBamp (serial UB-2016.05.01) Reference: right ear Ground: AFz Line frequency: 60.0 Hz Online filters: {‘bandpass’: [0.1, 200.0], ‘notch’: 60.0} Cap manufacturer: g.tec Cap model: g.GAMMAcap Auxiliary channels: EMG (8 ch)
Participants
Number of subjects: 45 Health status: healthy Age: mean=38.5, std=19.87, min=20.0, max=83.0 Gender distribution: female=26, male=19 Handedness: {‘right’: 40, ‘left’: 5} Species: human
Experimental Protocol
Paradigm: imagery Task type: motor_execution Number of classes: 4 Class labels: flexion, extension, radial_deviation, ulnar_deviation Trial duration: 10.0 s Study design: 4-direction wrist-pointing task with a 3-DoF wrist exoskeleton (Biomech Wrist). Flexion-extension controls horizontal cursor motion, radial-ulnar deviation controls vertical cursor motion. 8 runs x 40 trials (10 per movement per run). Feedback type: continuous visual Stimulus type: visual target Stimulus modalities: visual Primary modality: visual Synchronicity: cue-based Mode: offline
HED Event Annotations
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser flexion
├─ Sensory-event
└─ Label/flexion
extension
├─ Sensory-event
└─ Label/extension
radial_deviation
├─ Sensory-event
└─ Label/radial_deviation
ulnar_deviation
├─ Sensory-event
└─ Label/ulnar_deviation
Paradigm-Specific Parameters
Detected paradigm: motor_imagery Imagery tasks: flexion, extension, radial_deviation, ulnar_deviation Imagery duration: 2.5 s
Data Structure
Trials: 320 Trials per class: flexion=80, extension=80, radial_deviation=80, ulnar_deviation=80 Trials context: 45 subjects x >=8 runs x 40 trials (10 per class per run); the exact run count varies by subject (e.g. 9 for sub-01, 10 for sub-02), so per-subject totals exceed this minimum
Cross-Validation
Evaluation type: within_subject
BCI Application
Applications: motor_control, rehabilitation Environment: laboratory Online feedback: True
Tags
Pathology: Healthy Modality: Motor Type: Motor Execution
Documentation
DOI: 10.1038/s41597-026-07287-z License: CC-BY-4.0 Investigators: Jorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca Institution: Tecnologico de Monterrey Address: Monterrey, Nuevo Leon, Mexico Country: MX Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.29666735 Publication year: 2026
References
Perez-Blanco, J. G., Antelis-Ortiz, J. M., Hernandez-Rojas, L. G., & Lizarraga-Torreblanca, H. (2026). An EEG-EMG-kinematics dataset of wrist movements with a rehabilitation exoskeleton. Scientific Data. DOI: https://doi.org/10.1038/s41597-026-07287-z Data: https://doi.org/10.6084/m9.figshare.29666735 .. 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.
Ethics
Approved by the Institutional Research Ethics Committee of Tecnologico de Monterrey, approval number PR2024-05v1. The study was conducted in accordance with the Declaration of Helsinki, and written informed consent — including explicit agreement for open sharing of de-identified data — was obtained from all participants (Perez-Blanco et al. 2026, Sci. Data, DOI 10.1038/s41597-026-07287-z).
Verbatim from the source:
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committee of Tecnologico de Monterrey (approval number: PR2024-05v1).
Source: cached paper .paper-audit/PerezBlanco2026/paper-10_1038_s41597_026_07287_z.txt (Perez-Blanco et al. 2026, Sci. Data, DOI 10.1038/s41597-026-07287-z).
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000304) PerezBlanco2026 =============== Wrist-motion (4-direction pointing) motor-execution dataset [1]_. Dataset Overview —————-
Code: PerezBlanco2026 Paradigm: imagery DOI: 10.1038/s41597-026-07287-z Subjects: 45 Sessions per subject: 1 Events: flexion=1, extension=2, radial_deviation=3, ulnar_deviation=4 Trial interval: [0, 2.5] s Runs per session: 8 File format: EDF (BIDS, converted from BCI2000 .dat)
Acquisition#
Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=8, emg=8 Channel names: C3, C1, Cz, C2, C4, CP3, CPz, CP4 Montage: 10-10 Hardware: g.tec g.USBamp (serial UB-2016.05.01) Reference: right ear Ground: AFz Line frequency: 60.0 Hz Online filters: {‘bandpass’: [0.1, 200.0], ‘notch’: 60.0} Cap manufacturer: g.tec Cap model: g.GAMMAcap Auxiliary channels: EMG (8 ch)
Participants#
Number of subjects: 45 Health status: healthy Age: mean=38.5, std=19.87, min=20.0, max=83.0 Gender distribution: female=26, male=19 Handedness: {‘right’: 40, ‘left’: 5} Species: human
Experimental Protocol#
Paradigm: imagery Task type: motor_execution Number of classes: 4 Class labels: flexion, extension, radial_deviation, ulnar_deviation Trial duration: 10.0 s Study design: 4-direction wrist-pointing task with a 3-DoF wrist exoskeleton (Biomech Wrist). Flexion-extension controls horizontal cursor motion, radial-ulnar deviation controls vertical cursor motion. 8 runs x 40 trials (10 per movement per run). Feedback type: continuous visual Stimulus type: visual target Stimulus modalities: visual Primary modality: visual Synchronicity: cue-based Mode: offline
HED Event Annotations#
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser flexion
├─ Sensory-event └─ Label/flexion
- extension
├─ Sensory-event └─ Label/extension
- radial_deviation
├─ Sensory-event └─ Label/radial_deviation
- ulnar_deviation
├─ Sensory-event └─ Label/ulnar_deviation
Paradigm-Specific Parameters#
Detected paradigm: motor_imagery Imagery tasks: flexion, extension, radial_deviation, ulnar_deviation Imagery duration: 2.5 s
Data Structure#
Trials: 320 Trials per class: flexion=80, extension=80, radial_deviation=80, ulnar_deviation=80 Trials context: 45 subjects x >=8 runs x 40 trials (10 per class per run); the exact run count varies by subject (e.g. 9 for sub-01, 10 for sub-02), so per-subject totals exceed this minimum
Cross-Validation#
Evaluation type: within_subject
BCI Application#
Applications: motor_control, rehabilitation Environment: laboratory Online feedback: True
Documentation#
DOI: 10.1038/s41597-026-07287-z License: CC-BY-4.0 Investigators: Jorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca Institution: Tecnologico de Monterrey Address: Monterrey, Nuevo Leon, Mexico Country: MX Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.29666735 Publication year: 2026
References#
Perez-Blanco, J. G., Antelis-Ortiz, J. M., Hernandez-Rojas, L. G., & Lizarraga-Torreblanca, H. (2026). An EEG-EMG-kinematics dataset of wrist movements with a rehabilitation exoskeleton. Scientific Data. DOI: https://doi.org/10.1038/s41597-026-07287-z Data: https://doi.org/10.6084/m9.figshare.29666735 .. 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. ## Ethics Approved by the Institutional Research Ethics Committee of Tecnologico de Monterrey, approval number PR2024-05v1. The study was conducted in accordance with the Declaration of Helsinki, and written informed consent — including explicit agreement for open sharing of de-identified data — was obtained from all participants (Perez-Blanco et al. 2026, Sci. Data, DOI 10.1038/s41597-026-07287-z). Verbatim from the source: > The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committee of Tecnologico de Monterrey (approval number: PR2024-05v1). Source: cached paper .paper-audit/PerezBlanco2026/paper-10_1038_s41597_026_07287_z.txt (Perez-Blanco et al. 2026, Sci. Data, DOI 10.1038/s41597-026-07287-z).
License: CC-BY-4.0
Authors:
Jorge German Perez Blanco
Javier Mauricio Antelis Ortiz
Luis Guillermo Hernandez Rojas
Hector Lizarraga Torreblanca
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Age distribution by gender (n=45, range 38–38 yr, mean 38.0 yr)
Channel counts: 8 ch (n=448 recordings)
Sampling frequencies (Hz)
Total recording duration: 49 h
Signal · Electrodes & live trace#
Live trace viewer — sub-1 · ses-0 · task-imagery · run-0
Showing one representative recording out of
54 subjects and 448 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 · 8 sensors — 8 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Jorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000304,
title = {PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset},
author = {Jorge German Perez Blanco and Javier Mauricio Antelis Ortiz and Luis Guillermo Hernandez Rojas and Hector Lizarraga Torreblanca},
doi = {10.82901/nemar.nm000304},
url = {https://doi.org/10.82901/nemar.nm000304},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000304(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset
- Study:
nm000304(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000304.Modality:
eeg; Subject type:Unknown. Subjects: 54; recordings: 448; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/nm000304 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000304 DOI: https://doi.org/10.82901/nemar.nm000304
Examples
>>> from eegdash.dataset import NM000304 >>> dataset = NM000304(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 nm000304 to reproduce the tutorial on this dataset.
Citation
Jorge German Perez Blanco, Javier Mauricio Antelis Ortiz, Luis Guillermo Hernandez Rojas, Hector Lizarraga Torreblanca (2026). PerezBlanco2026: Wrist-motion (4-direction pointing) motor-execution dataset. 10.82901/nemar.nm000304
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000304.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset