NM000129: eeg dataset, 70 subjects#

Name: Liu2020 – BETA SSVEP benchmark dataset
Published: 2019-01-01
License: Non-commercial research use

Liu2020 – BETA SSVEP benchmark dataset

Access recordings and metadata through EEGDash.

Citation: Bingchuan Liu, Xiaoshan Huang, Yijun Wang, Xiaogang Chen, Xiaorong Gao (2019). Liu2020 – BETA SSVEP benchmark dataset.

Modality: eeg Subjects: 70 Recordings: 70 License: Non-commercial research use Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000129

dataset = NM000129(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 = NM000129(cache_dir="./data", subject="01")

Advanced query

dataset = NM000129(
    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{nm000129,
  title = {Liu2020 – BETA SSVEP benchmark dataset},
  author = {Bingchuan Liu and Xiaoshan Huang and Yijun Wang and Xiaogang Chen and Xiaorong Gao},
}

About This Dataset#

BETA SSVEP benchmark dataset

BETA SSVEP benchmark dataset.

Dataset Overview

Code: Liu2020BETA
Paradigm: ssvep
DOI: 10.3389/fnins.2020.00627

Dataset Information#

Dataset ID	`NM000129`
Title	Liu2020 – BETA SSVEP benchmark dataset
Author (year)	`Liu2020`
Canonical	—
Importable as	`NM000129`, `Liu2020`
Year	2019
Authors	Bingchuan Liu, Xiaoshan Huang, Yijun Wang, Xiaogang Chen, Xiaorong Gao
License	Non-commercial research use
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: 70
Recordings: 70
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 250.0
Duration (hours): 13.022144444444445

Electrode Layout#

Electrode layout — EEG · 62 sensors — 62 channels

Dataset Statistics#

Age distribution (n=70, range 10–64 yr)

101520253035404560

Sex distribution

28

42

Female Male Total: 70

Channel counts: 64 ch (n=70 recordings)

Sampling frequencies: 250.0 Hz (n=70 recordings)

Total recording duration: 13 h 1 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 NM000129 class to access this dataset programmatically.

class eegdash.dataset.NM000129(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Liu2020 – BETA SSVEP benchmark dataset

Study:: nm000129 (NeMAR)
Author (year):: Liu2020
Canonical:: —

Also importable as: NM000129, Liu2020.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 70; recordings: 70; 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/nm000129 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000129

Examples

>>> from eegdash.dataset import NM000129
>>> dataset = NM000129(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)