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. 10.82901/nemar.nm000131

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

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
  doi = {10.82901/nemar.nm000131},
  url = {https://doi.org/10.82901/nemar.nm000131},
}

About This Dataset#

DOI

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

View full README

DOI

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) 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	10.82901/nemar.nm000131
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

Electrode Layout#

Electrode layout — EEG · 31 sensors — 31 channels

Dataset Statistics#

Channel counts: 31 ch (n=22 recordings)

Sampling frequencies: 1000.0 Hz (n=22 recordings)

Total recording duration: 6 h 9 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.

HED event descriptors word cloud

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 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 DOI: https://doi.org/10.82901/nemar.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: 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)