NM000211: eeg dataset, 15 subjects#

RSVP ERP dataset for authentication from Zhang et al 2025

Access recordings and metadata through EEGDash.

Citation: Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao, Chen Yang (2025). RSVP ERP dataset for authentication from Zhang et al 2025.

Modality: eeg Subjects: 15 Recordings: 240 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000211

dataset = NM000211(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000211(cache_dir="./data", subject="01")

Advanced query

dataset = NM000211(
    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{nm000211,
  title = {RSVP ERP dataset for authentication from Zhang et al 2025},
  author = {Yufeng Zhang and Hongxin Zhang and Yixuan Li and Yijun Wang and Xiaorong Gao and Chen Yang},
}

About This Dataset#

RSVP ERP dataset for authentication from Zhang et al 2025

RSVP ERP dataset for authentication from Zhang et al 2025.

Dataset Overview

  • Code: Zhang2025

  • Paradigm: p300

  • DOI: 10.1038/s41597-025-05378-x

View full README

RSVP ERP dataset for authentication from Zhang et al 2025

RSVP ERP dataset for authentication from Zhang et al 2025.

Dataset Overview

  • Code: Zhang2025

  • Paradigm: p300

  • DOI: 10.1038/s41597-025-05378-x

  • Subjects: 15

  • Sessions per subject: 4

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 0.6] s

  • Runs per session: 4

  • File format: MATLAB (HDF5)

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 57

  • Channel types: eeg=57

  • 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, PO7, PO8, Oz, O1, O2

  • Montage: standard_1020

  • Hardware: Neuracle Neusen

  • Reference: CPz

  • Ground: AFz

  • Line frequency: 50.0 Hz

Participants

  • Number of subjects: 15

  • Health status: healthy

  • Age: min=22, max=26

  • Gender distribution: female=6, male=9

  • Handedness: all right-handed

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 1.0 s

  • Study design: RSVP face authentication; self-face vs AI-generated faces; 4 sessions over 200 days (longitudinal)

  • Feedback type: none

  • Stimulus type: RSVP face images

  • Stimulus modalities: visual

  • Primary modality: visual

  • Mode: offline

HED Event Annotations

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

Target

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

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

  • Detected paradigm: p300

  • Stimulus onset asynchrony: 100.0 ms

Data Structure

  • Trials: ~160 target + ~6240 nontarget per session

  • Trials context: per session (4 blocks x 8 sequences x 200 images)

Signal Processing

  • Classifiers: HDCA

  • Feature extraction: HDCA

  • Frequency bands: ERP_dominant=[0.0, 10.0] Hz

Cross-Validation

  • Evaluation type: within_subject

BCI Application

  • Applications: identity_authentication, target_detection

  • Environment: laboratory

Tags

  • Pathology: Healthy

  • Modality: ERP

  • Type: RSVP

Documentation

  • DOI: 10.1038/s41597-025-05378-x

  • License: CC-BY-NC-ND-4.0

  • Investigators: Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao, Chen Yang

  • Institution: Beijing University of Posts and Telecommunications

  • Country: CN

  • Data URL: https://figshare.com/articles/dataset/27201003

  • Publication year: 2025

References

Zhang, Y., Zhang, H., Li, Y., Wang, Y., Gao, X., & Yang, C. (2025). A longitudinal EEG dataset of event-related potential for EEG-based identity authentication. Scientific Data, 12, 1069. https://doi.org/10.1038/s41597-025-05378-x 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000211

Title

RSVP ERP dataset for authentication from Zhang et al 2025

Author (year)

Zhang2025_RSVP

Canonical

—

Importable as

NM000211, Zhang2025_RSVP

Year

2025

Authors

Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao, Chen Yang

License

CC-BY-NC-ND-4.0

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 15

  • Recordings: 240

  • Tasks: 1

Channels & sampling rate

  • Channels: 57

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 15.022525

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 8.7 GB

  • File count: 240

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

Electrode Layout#

Electrode layout — EEG · 57 sensors — 57 channels

Dataset Statistics#

Channel counts: 57 ch (n=240 recordings)

Sampling frequencies: 1000.0 Hz (n=240 recordings)

Total recording duration: 15 h 1 min

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.

NEMAR pipeline statistics — NM000211
HED event descriptors word cloud HED event descriptors word cloud — NM000211

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.

Files: —
Size: —
Subjects: —
Click to load file structure…

API Reference#

Use the NM000211 class to access this dataset programmatically.

class eegdash.dataset.NM000211(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

RSVP ERP dataset for authentication from Zhang et al 2025

Study:

nm000211 (NeMAR)

Author (year):

Zhang2025_RSVP

Canonical:

—

Also importable as: NM000211, Zhang2025_RSVP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 240; 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/nm000211 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000211

Examples

>>> from eegdash.dataset import NM000211
>>> dataset = NM000211(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.

See Also#