NM000124: eeg dataset, 24 subjects#

Name: Han2024 – SSVEP fatigue dataset with two frequency paradigms
Published: 2019
License: CC BY 4.0

Han2024 – SSVEP fatigue dataset with two frequency paradigms

Access recordings and metadata through EEGDash.

Citation: Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming (2019). Han2024 – SSVEP fatigue dataset with two frequency paradigms.

Modality: eeg Subjects: 24 Recordings: 48 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000124

dataset = NM000124(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 = NM000124(cache_dir="./data", subject="01")

Advanced query

dataset = NM000124(
    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{nm000124,
  title = {Han2024 – SSVEP fatigue dataset with two frequency paradigms},
  author = {Yuheng Han and Yufeng Ke and Ruiyan Wang and Tao Wang and Dong Ming},
}

About This Dataset#

SSVEP fatigue dataset with two frequency paradigms

SSVEP fatigue dataset with two frequency paradigms.

Dataset Overview

Code: Han2024Fatigue
Paradigm: ssvep
DOI: 10.1109/TNSRE.2024.3380635

Dataset Information#

Dataset ID	`NM000124`
Title	Han2024 – SSVEP fatigue dataset with two frequency paradigms
Author (year)	`Han2024`
Canonical	—
Importable as	`NM000124`, `Han2024`
Year	2019
Authors	Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming
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: 24
Recordings: 48
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 19.839986666666668

API Reference#

Use the NM000124 class to access this dataset programmatically.

class eegdash.dataset.NM000124(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Han2024 – SSVEP fatigue dataset with two frequency paradigms

Study:: nm000124 (NeMAR)
Author (year):: Han2024
Canonical:: —

Also importable as: NM000124, Han2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 48; 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/nm000124 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000124

Examples

>>> from eegdash.dataset import NM000124
>>> dataset = NM000124(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