NM000128: eeg dataset, 59 subjects#

Name: Dong2023 – 59-subject 40-class SSVEP dataset
Published: 2019
License: CC BY-NC 4.0

Dong2023 – 59-subject 40-class SSVEP dataset

Access recordings and metadata through EEGDash.

Citation: Yue Dong, Sen Tian (2019). Dong2023 – 59-subject 40-class SSVEP dataset.

Modality: eeg Subjects: 59 Recordings: 59 License: CC BY-NC 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000128

dataset = NM000128(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 = NM000128(cache_dir="./data", subject="01")

Advanced query

dataset = NM000128(
    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{nm000128,
  title = {Dong2023 – 59-subject 40-class SSVEP dataset},
  author = {Yue Dong and Sen Tian},
}

About This Dataset#

59-subject 40-class SSVEP dataset

59-subject 40-class SSVEP dataset.

Dataset Overview

Code: Dong2023
Paradigm: ssvep
DOI: 10.26599/BSA.2023.9050020

Dataset Information#

Dataset ID	`NM000128`
Title	Dong2023 – 59-subject 40-class SSVEP dataset
Author (year)	`Dong2023`
Canonical	—
Importable as	`NM000128`, `Dong2023`
Year	2019
Authors	Yue Dong, Sen Tian
License	CC BY-NC 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: 59
Recordings: 59
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 250.0
Duration (hours): 14.159934444444444

API Reference#

Use the NM000128 class to access this dataset programmatically.

class eegdash.dataset.NM000128(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Dong2023 – 59-subject 40-class SSVEP dataset

Study:: nm000128 (NeMAR)
Author (year):: Dong2023
Canonical:: —

Also importable as: NM000128, Dong2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 59; recordings: 59; 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/nm000128 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000128

Examples

>>> from eegdash.dataset import NM000128
>>> dataset = NM000128(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