NM000211: eeg dataset, 15 subjects#

Name: RSVP ERP dataset for authentication from Zhang et al 2025
Published: 2025
License: CC-BY-NC-ND-4.0

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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000211`
Title	RSVP ERP dataset for authentication from Zhang et al 2025
Author (year)	`Zhang2025_RSVP`
Canonical	`Zhang2025`
Importable as	`NM000211`, `Zhang2025_RSVP`, `Zhang2025`
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

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:: Zhang2025

Also importable as: NM000211, Zhang2025_RSVP, Zhang2025.

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None