EEGdash›NeMAR›NM000127

Iss. 127 · 40 subjects · 240 recordings · CC BY 4.0

Dataset Brief · Kim2025 – 40-class beta-range SSVEP speller dataset

NM000127: eeg dataset, 40 subjects#

Kim2025 – 40-class beta-range SSVEP speller dataset

Citation: Heegyu Kim, Kyungho Won, Minkyu Ahn, Sung Chan Jun (2019). Kim2025 – 40-class beta-range SSVEP speller dataset. 10.82901/nemar.nm000127

40-participant EEG dataset — Kim2025 – 40-class beta-range SSVEP speller dataset.

Data & curation Heegyu Kim · Kyungho Won · Minkyu Ahn · Sung Chan Jun
Year 2019 · Distributed via NeMAR

EEG · 31 ch1024 HzBIDS 1.9.0Task · ssvep6 sessionsHealthyVisualPerception

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 NM000127

dataset = NM000127(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 = NM000127(cache_dir="./data", subject="01")

Advanced query

dataset = NM000127(
    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{nm000127,
  title = {Kim2025 – 40-class beta-range SSVEP speller dataset},
  author = {Heegyu Kim and Kyungho Won and Minkyu Ahn and Sung Chan Jun},
  doi = {10.82901/nemar.nm000127},
  url = {https://doi.org/10.82901/nemar.nm000127},
}

§ 02Study · The README

About This Dataset#

40-class beta-range SSVEP speller dataset.

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

DOI

40-class beta-range SSVEP speller dataset

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=40, range 23–23 yr, mean 22.0 yr)

20

Female · 15Male · 25

Sex composition

Female
15
Male
25
F : M ratio
0.60 : 1

38% female · n = 40 subjects with reported sex.

HandednessRight · 35Left · 4

Channel counts: 31 ch (n=240 recordings)

Sampling frequencies: 1024.0 Hz (n=240 recordings)

Total recording duration: 18 h 55 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 31 ch · EEG · 1024 Hz · 40 subjects, 240 recordings

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

Showing one representative recording out of 40 subjects and 240 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 · 31 sensors — 31 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.NM000127(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kim2025_SSVEP

Canonical—

Importable asNM000127 · Kim2025_SSVEP

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

class eegdash.dataset.NM000127(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Kim2025 – 40-class beta-range SSVEP speller dataset

Study:: nm000127 (NeMAR)
Author (year):: Kim2025_SSVEP
Canonical:: —

Also importable as: NM000127, Kim2025_SSVEP.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 40; recordings: 240; 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/nm000127 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000127 DOI: https://doi.org/10.82901/nemar.nm000127

Examples

>>> from eegdash.dataset import NM000127
>>> dataset = NM000127(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 — NM000127.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 nm000127 to reproduce the tutorial on this dataset.

Citation

Heegyu Kim, Kyungho Won, Minkyu Ahn, Sung Chan Jun (2019). Kim2025 – 40-class beta-range SSVEP speller dataset. 10.82901/nemar.nm000127

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000127.

Related & sibling datasets

NM000124EEG · 24 subj NM000216EEG · 43 subj NM000214EEG · 30 subj NM000310EEG · 11 subj NM000313EEG · 24 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC BY 4.0 · 10.82901/nemar.nm000127

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000127`
Title	Kim2025 – 40-class beta-range SSVEP speller dataset
Author (year)	`Kim2025_SSVEP`
Canonical	—
Importable as	`NM000127`, `Kim2025_SSVEP`
Year	2019
Authors	Heegyu Kim, Kyungho Won, Minkyu Ahn, Sung Chan Jun
License	CC BY 4.0
Citation / DOI	10.82901/nemar.nm000127
Source links	OpenNeuro \| NeMAR \| Source URL

NM000127: eeg dataset, 40 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#