NM000131: eeg dataset, 8 subjects#

Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs

Access recordings and metadata through EEGDash.

Citation: Lu Wang, Zhenhao Zhang, Dan Han, Zhijun Zhang, Zhifang Liu, Wei Liu (2019). Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs.

Modality: eeg Subjects: 8 Recordings: 22 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000131

dataset = NM000131(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000131(cache_dir="./data", subject="01")

Advanced query

dataset = NM000131(
    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{nm000131,
  title = {Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs},
  author = {Lu Wang and Zhenhao Zhang and Dan Han and Zhijun Zhang and Zhifang Liu and Wei Liu},
}

About This Dataset#

Combined SSVEP dataset with single stimulus location for two inputs

Combined SSVEP dataset with single stimulus location for two inputs.

Dataset Overview

  • Code: Wang2021Combined

  • Paradigm: ssvep

  • DOI: 10.1111/ejn.15030

View full README

Combined SSVEP dataset with single stimulus location for two inputs

Combined SSVEP dataset with single stimulus location for two inputs.

Dataset Overview

  • Code: Wang2021Combined

  • Paradigm: ssvep

  • DOI: 10.1111/ejn.15030

  • Subjects: 8

  • Sessions per subject: 1

  • Events: 14.17=1, 12.14=2, 9.44=3, 7.73=4

  • Trial interval: [0.0, 5.0] s

  • File format: CNT

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 31

  • Channel types: eeg=31, eog=2

  • Montage: standard_1005

  • Hardware: eego mylab (ANT Neuro)

  • Line frequency: 50.0 Hz

Participants

  • Number of subjects: 8

  • Health status: healthy

Experimental Protocol

  • Paradigm: ssvep

  • Task type: covert_attention

  • Number of classes: 4

  • Class labels: 14.17, 12.14, 9.44, 7.73

  • Trial duration: 5.0 s

  • Study design: One-to-two combined SSVEP with overlapping stimuli

  • Feedback type: none

  • Stimulus type: overlapping SSVEP arrows (CRT 85 Hz)

  • 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

14.17

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/14_17

12.14

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/12_14

9.44

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/9_44

7.73

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/7_73

Paradigm-Specific Parameters

  • Detected paradigm: ssvep

  • Stimulus frequencies: [14.17, 12.14, 9.44, 7.73] Hz

Data Structure

  • Blocks per session: 2

BCI Application

  • Environment: lab

Tags

  • Pathology: healthy

  • Modality: visual

  • Type: perception

Documentation

  • DOI: 10.1111/ejn.15030

  • License: CC BY 4.0

  • Investigators: Lu Wang, Zhenhao Zhang, Dan Han, Zhijun Zhang, Zhifang Liu, Wei Liu

  • Senior author: Zhijun Zhang

  • Institution: Shandong University

  • Country: CN

  • Repository: Zenodo

  • Data URL: https://zenodo.org/records/18873228

  • Publication year: 2021

References

L. Wang, Z. Zhang, D. Han, Z. Zhang, Z. Liu, and W. Liu, “Single stimulus location for two inputs: A combined brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP),” European Journal of Neuroscience, vol. 53, no. 3, pp. 861-875, 2021. DOI: 10.1111/ejn.15030 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 1.4.3 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000131

Title

Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs

Author (year)

Wang2021

Canonical

—

Importable as

NM000131, Wang2021

Year

2019

Authors

Lu Wang, Zhenhao Zhang, Dan Han, Zhijun Zhang, Zhifang Liu, Wei Liu

License

CC BY 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: 8

  • Recordings: 22

  • Tasks: 1

Channels & sampling rate

  • Channels: 31

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 6.1615825

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 2.6 GB

  • File count: 22

  • Format: BIDS

License & citation

  • License: CC BY 4.0

  • DOI: —

Provenance

API Reference#

Use the NM000131 class to access this dataset programmatically.

class eegdash.dataset.NM000131(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs

Study:

nm000131 (NeMAR)

Author (year):

Wang2021

Canonical:

—

Also importable as: NM000131, Wang2021.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 8; recordings: 22; 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/nm000131 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000131

Examples

>>> from eegdash.dataset import NM000131
>>> dataset = NM000131(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#