EEGdash›NeMAR›NM000201

Iss. 201 · 24 subjects · 113 recordings · CC BY 4.0

Dataset Brief · ERP paradigm of the Mobile BCI dataset

NM000201: eeg dataset, 24 subjects#

ERP paradigm of the Mobile BCI dataset

Citation: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee (2019). ERP paradigm of the Mobile BCI dataset.

24-participant EEG dataset — ERP paradigm of the Mobile BCI dataset.

Data & curation Young-Eun Lee · Gi-Hwan Shin · Minji Lee · Seong-Whan Lee
Year 2019 · Distributed via NeMAR
Funding IITP No. 2017-0-00451 · IITP No. 2015-0-00185 · + 1 more

EEG · 48 (108), 73 (5) ch500 Hz · mixedBIDS 1.9.0Task · p3005 sessionsHealthyVisualAttention

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 NM000201

dataset = NM000201(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 = NM000201(cache_dir="./data", subject="01")

Advanced query

dataset = NM000201(
    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{nm000201,
  title = {ERP paradigm of the Mobile BCI dataset},
  author = {Young-Eun Lee and Gi-Hwan Shin and Minji Lee and Seong-Whan Lee},
}

§ 02Study · The README

About This Dataset#

ERP paradigm of the Mobile BCI dataset.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

ERP paradigm of the Mobile BCI dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=24, range 19–32 yr, mean 24.5 yr)

15202530

Female · 10Male · 14

Sex composition

Female
10
Male
14
F : M ratio
0.71 : 1

42% female · n = 24 subjects with reported sex.

Channel counts (ch)

4873

Sampling frequencies (Hz)

100500

Total recording duration: 22 h 8 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 48 (108), 73 (5) ch · EEG · 500 Hz · mixed · 24 subjects, 113 recordings

Live trace viewer — sub-13 · ses-4 · task-p300 · run-0

Showing one representative recording out of 24 subjects and 113 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.NM000201(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Lee2021_ERP

Canonical—

Importable asNM000201 · Lee2021_ERP

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

class eegdash.dataset.NM000201(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

ERP paradigm of the Mobile BCI dataset

Study:: nm000201 (NeMAR)
Author (year):: Lee2021_ERP
Canonical:: —

Also importable as: NM000201, Lee2021_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 113; 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/nm000201 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000201

Examples

>>> from eegdash.dataset import NM000201
>>> dataset = NM000201(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 — NM000201.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 nm000201 to reproduce the tutorial on this dataset.

Citation

Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee (2019). ERP paradigm of the Mobile BCI dataset.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000206EEG · 15 subj NM000343EEG · 15 subj NM000264EEG · 24 subj NM000168EEG · 6 subj NM000346EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC BY 4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000201`
Title	ERP paradigm of the Mobile BCI dataset
Author (year)	`Lee2021_ERP`
Canonical	—
Importable as	`NM000201`, `Lee2021_ERP`
Year	2019
Authors	Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee
License	CC BY 4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000201: eeg dataset, 24 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#