EEGdash›NeMAR›NM000273

Iss. 273 · 54 subjects · 108 recordings · CC-BY-4.0

Dataset Brief · OpenBMI SSVEP EEG dataset (Lee et al. 2019)

NM000273: eeg dataset, 54 subjects#

OpenBMI SSVEP EEG dataset (Lee et al. 2019)

Access recordings and metadata through EEGDash.

Citation: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee (20). OpenBMI SSVEP EEG dataset (Lee et al. 2019). 10.82901/nemar.nm000273

Modality: eeg Subjects: 54 Recordings: 108 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

54-participant EEG dataset — OpenBMI SSVEP EEG dataset (Lee et al. 2019).

Data & curation Min-Ho Lee · O-Yeon Kwon · Yong-Jeong Kim · Hong-Kyung Kim · Young-Eun Lee · John Williamson · …
Senior author Seong-Whan Lee
Year 20 · Distributed via NeMAR

EEG · 62 ch1000 HzBIDS 1.7.0Task · ssvep2 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000273

dataset = NM000273(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 = NM000273(cache_dir="./data", subject="01")

Advanced query

dataset = NM000273(
    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{nm000273,
  title = {OpenBMI SSVEP EEG dataset (Lee et al. 2019)},
  author = {Min-Ho Lee and O-Yeon Kwon and Yong-Jeong Kim and Hong-Kyung Kim and Young-Eun Lee and John Williamson and Siamac Fazli and Seong-Whan Lee},
  doi = {10.82901/nemar.nm000273},
  url = {https://doi.org/10.82901/nemar.nm000273},
}

§ 02Study · The README

About This Dataset#

A derivative dataset of SSVEP (steady-state visually evoked potential) recordings processed and organized using the Mother of All BCI Benchmarks (MOABB) framework. This dataset represents EEG data formatted according to BIDS standards, enabling standardized analysis and benchmarking of brain-computer interface paradigms based on steady-state visual stimulation. The dataset is derived from the Lee2019 source dataset (DOI: 10.5524/100542) and has been converted to BIDS format using MNE-BIDS tools. The dataset contains EEG recordings from multiple subjects across multiple sessions with SSVEP stimulation paradigms.

See the primary publication for details on data collection and experimental protocol.

DOI

OpenBMI SSVEP EEG dataset (Lee et al. 2019)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000273-blue)](https://doi.org/10.82901/nemar.nm000273) # OpenBMI SSVEP EEG dataset (Lee et al. 2019) ## Overview A derivative dataset of SSVEP (steady-state visually evoked potential) recordings processed and organized using the Mother of All BCI Benchmarks (MOABB) framework. This dataset represents EEG data formatted according to BIDS standards, enabling standardized analysis and benchmarking of brain-computer interface paradigms based on steady-state visual stimulation. The dataset is derived from the Lee2019 source dataset (DOI: 10.5524/100542) and has been converted to BIDS format using MNE-BIDS tools. The dataset contains EEG recordings from multiple subjects across multiple sessions with SSVEP stimulation paradigms. ## Dataset Summary | Property | Value | |---|—| | Subjects | 54 | | Channels | 62 | | Classes | 4 | | Trial length | 4 s | | Sampling frequency | 1000 Hz | | Sessions | 2 | | Total trials | 21600 | | Paradigm | SSVEP | ## Data Collection Methods See the primary publication for details on data collection and experimental protocol. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Lee2019_SSVEP from moabb.paradigms import SSVEP paradigm = SSVEP() dataset = Lee2019_SSVEP() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Lee2019_SSVEP.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.1093/gigascience/giz002](https://doi.org/10.1093/gigascience/giz002) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC-BY-4.0

Authors:

Min-Ho Lee
O-Yeon Kwon
Yong-Jeong Kim
Hong-Kyung Kim
Young-Eun Lee
… and 3 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000273

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 62 ch (n=108 recordings)

Sampling frequencies: 1000.0 Hz (n=108 recordings)

Total recording duration: 40 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 62 ch · EEG · 1000 Hz · 54 subjects, 108 recordings

Live trace viewer — sub-1 · ses-0 · task-ssvep · run-1

Showing one representative recording out of 54 subjects and 108 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 62 sensors — 62 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000273(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000273

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000273(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

OpenBMI SSVEP EEG dataset (Lee et al. 2019)

Study:: nm000273 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000273.

Modality: eeg; Subject type: Unknown. Subjects: 54; recordings: 108; 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/nm000273 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000273 DOI: https://doi.org/10.82901/nemar.nm000273

Examples

>>> from eegdash.dataset import NM000273
>>> dataset = NM000273(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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000273.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000273 to reproduce the tutorial on this dataset.

Citation

Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, … (20). OpenBMI SSVEP EEG dataset (Lee et al. 2019). 10.82901/nemar.nm000273

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000273.

Related & sibling datasets

ON004279EEG · 56 subj ON007322EEG · 57 subj ON004626EEG · 52 subj ON004718EEG · 51 subj ON003825EEG · 50 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000273

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000273`
Title	OpenBMI SSVEP EEG dataset (Lee et al. 2019)
Author (year)	—
Canonical	—
Importable as	`NM000273`
Year	20
Authors	Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000273
Source links	OpenNeuro \| NeMAR \| Source URL

NM000273: eeg dataset, 54 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#