NM000130: eeg dataset, 100 subjects#

Liu2022 – eldBETA SSVEP benchmark dataset for elderly population

Access recordings and metadata through EEGDash.

Citation: Bingchuan Liu, Yijun Wang, Xiaorong Gao, Xiaogang Chen (2019). Liu2022 – eldBETA SSVEP benchmark dataset for elderly population. 10.82901/nemar.nm000130

Modality: eeg Subjects: 100 Recordings: 700 License: CC BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000130

dataset = NM000130(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 = NM000130(cache_dir="./data", subject="01")

Advanced query

dataset = NM000130(
    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{nm000130,
  title = {Liu2022 – eldBETA SSVEP benchmark dataset for elderly population},
  author = {Bingchuan Liu and Yijun Wang and Xiaorong Gao and Xiaogang Chen},
  doi = {10.82901/nemar.nm000130},
  url = {https://doi.org/10.82901/nemar.nm000130},
}

About This Dataset#

DOI

eldBETA SSVEP benchmark dataset for elderly population

eldBETA SSVEP benchmark dataset for elderly population.

Dataset Overview

Code: Liu2022EldBETA
Paradigm: ssvep

Dataset Information#

Dataset ID	`NM000130`
Title	Liu2022 – eldBETA SSVEP benchmark dataset for elderly population
Author (year)	`Liu2022`
Canonical	—
Importable as	`NM000130`, `Liu2022`
Year	2019
Authors	Bingchuan Liu, Yijun Wang, Xiaorong Gao, Xiaogang Chen
License	CC BY 4.0
Citation / DOI	10.82901/nemar.nm000130
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: 100
Recordings: 700
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 20.17517222222222

Electrode Layout#

Electrode layout — EEG · 57 sensors — 57 channels

Dataset Statistics#

Age distribution (n=100, range 70–70 yr)

70

Sex distribution

67

33

Female Male Total: 100

Channel counts: 64 ch (n=700 recordings)

Sampling frequencies: 1000.0 Hz (n=700 recordings)

Total recording duration: 20 h 10 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 NM000130 class to access this dataset programmatically.

class eegdash.dataset.NM000130(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Liu2022 – eldBETA SSVEP benchmark dataset for elderly population

Study:: nm000130 (NeMAR)
Author (year):: Liu2022
Canonical:: —

Also importable as: NM000130, Liu2022.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 100; recordings: 700; 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/nm000130 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000130 DOI: https://doi.org/10.82901/nemar.nm000130

Examples

>>> from eegdash.dataset import NM000130
>>> dataset = NM000130(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)