NM000126: eeg dataset, 34 subjects#

Name: Wang2016 – SSVEP Wang 2016 dataset
Published: 2016
License: CC-BY-4.0

Wang2016 – SSVEP Wang 2016 dataset

Access recordings and metadata through EEGDash.

Citation: Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao (2016). Wang2016 – SSVEP Wang 2016 dataset.

Modality: eeg Subjects: 34 Recordings: 34 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000126

dataset = NM000126(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 = NM000126(cache_dir="./data", subject="01")

Advanced query

dataset = NM000126(
    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{nm000126,
  title = {Wang2016 – SSVEP Wang 2016 dataset},
  author = {Yijun Wang and Xiaogang Chen and Xiaorong Gao and Shangkai Gao},
}

About This Dataset#

SSVEP Wang 2016 dataset

SSVEP Wang 2016 dataset.

Dataset Overview

Code: Wang2016
Paradigm: ssvep
DOI: 10.1109/TNSRE.2016.2627556

Dataset Information#

Dataset ID	`NM000126`
Title	Wang2016 – SSVEP Wang 2016 dataset
Author (year)	`Wang2016`
Canonical	—
Importable as	`NM000126`, `Wang2016`
Year	2016
Authors	Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao
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: 34
Recordings: 34
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 250.0
Duration (hours): 14.506628888888889

API Reference#

Use the NM000126 class to access this dataset programmatically.

class eegdash.dataset.NM000126(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Wang2016 – SSVEP Wang 2016 dataset

Study:: nm000126 (NeMAR)
Author (year):: Wang2016
Canonical:: —

Also importable as: NM000126, Wang2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 34; recordings: 34; 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/nm000126 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000126

Examples

>>> from eegdash.dataset import NM000126
>>> dataset = NM000126(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