NM000310: eeg dataset, 11 subjects#

GuttmannFlury2025-SSVEP

Access recordings and metadata through EEGDash.

Citation: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu (2025). GuttmannFlury2025-SSVEP. 10.1038/s41597-025-04861-9

Modality: eeg Subjects: 11 Recordings: 26 License: CC0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000310

dataset = NM000310(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000310(cache_dir="./data", subject="01")

Advanced query

dataset = NM000310(
    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{nm000310,
  title = {GuttmannFlury2025-SSVEP},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
  doi = {10.1038/s41597-025-04861-9},
  url = {https://doi.org/10.1038/s41597-025-04861-9},
}

About This Dataset#

GuttmannFlury2025-SSVEP

Eye-BCI multimodal SSVEP dataset from Guttmann-Flury et al 2025.

Dataset Overview

Code: GuttmannFlury2025-SSVEP Paradigm: ssvep DOI: 10.1038/s41597-025-04861-9

View full README

GuttmannFlury2025-SSVEP

Eye-BCI multimodal SSVEP dataset from Guttmann-Flury et al 2025.

Dataset Overview

Code: GuttmannFlury2025-SSVEP Paradigm: ssvep DOI: 10.1038/s41597-025-04861-9 Subjects: 31 Sessions per subject: 3 Events: 10.0=1, 11.0=2, 12.0=3, 13.0=4 Trial interval: [0, 5] s File format: BDF

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 66 Channel types: eeg=64, eog=1, stim=1 Channel names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, O1, OZ, O2, CB1, CB2 Montage: standard_1005 Hardware: Neuroscan Quik-Cap 65-ch, SynAmps2 Reference: right mastoid (M1) Ground: forehead Sensor type: Ag/AgCl Line frequency: 50.0 Hz Online filters: {‘highpass_time_constant_s’: 10}

Participants

Number of subjects: 31 Health status: healthy Age: mean=28.3, min=20.0, max=57.0 Gender distribution: female=11, male=20 Species: human

Experimental Protocol

Paradigm: ssvep Number of classes: 4 Class labels: 10.0, 11.0, 12.0, 13.0 Trial duration: 7.0 s Study design: Multi-paradigm BCI (MI/ME/SSVEP/P300). SSVEP: 4-class frequency flickering, 48 trials/session, up to 3 sessions per subject. Feedback type: none Stimulus type: flickering LED Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser 10.0

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/10_0

11.0

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/11_0

12.0

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/12_0

13.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/13_0

Paradigm-Specific Parameters

Detected paradigm: ssvep Stimulus frequencies: [8.0, 10.0, 12.0, 15.0] Hz

Data Structure

Trials: 3024 Trials context: 63 sessions x 48 trials = 3024

BCI Application

Applications: communication Environment: laboratory

Tags

Pathology: Healthy Modality: Visual Type: Research

Documentation

DOI: 10.1038/s41597-025-04861-9 License: CC0 Investigators: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu Institution: Shanghai Jiao Tong University Country: CN Publication year: 2025

References

Guttmann-Flury, E., Sheng, X., & Zhu, X. (2025). Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. Scientific Data, 12, 587. https://doi.org/10.1038/s41597-025-04861-9 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

NM000310

Title

GuttmannFlury2025-SSVEP

Author (year)

GuttmannFlury2025_SSVEP

Canonical

—

Importable as

NM000310, GuttmannFlury2025_SSVEP

Year

2025

Authors

Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu

License

CC0

Citation / DOI

doi:10.1038/s41597-025-04861-9

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000310,
  title = {GuttmannFlury2025-SSVEP},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
  doi = {10.1038/s41597-025-04861-9},
  url = {https://doi.org/10.1038/s41597-025-04861-9},
}

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: 11

  • Recordings: 26

  • Tasks: 1

Channels & sampling rate

  • Channels: 65

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 3.1566594444444447

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 2.1 GB

  • File count: 26

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.1038/s41597-025-04861-9

Provenance

API Reference#

Use the NM000310 class to access this dataset programmatically.

class eegdash.dataset.NM000310(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

GuttmannFlury2025-SSVEP

Study:

nm000310 (NeMAR)

Author (year):

GuttmannFlury2025_SSVEP

Canonical:

—

Also importable as: NM000310, GuttmannFlury2025_SSVEP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 26; 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/nm000310 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000310 DOI: https://doi.org/10.1038/s41597-025-04861-9

Examples

>>> from eegdash.dataset import NM000310
>>> dataset = NM000310(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

See Also#