NM000127: eeg dataset, 40 subjects#

Name: Kim2025 – 40-class beta-range SSVEP speller dataset
Published: 2019
License: CC BY 4.0

Kim2025 – 40-class beta-range SSVEP speller dataset

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 40 Recordings: 240 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset#

40-class beta-range SSVEP speller dataset

40-class beta-range SSVEP speller dataset.

Dataset Overview

Code: Kim2025BetaRange
Paradigm: ssvep
DOI: 10.1038/s41597-025-06032-2

Dataset Information#

Dataset ID	`NM000127`
Title	Kim2025 – 40-class beta-range SSVEP speller dataset
Author (year)	`Kim2025_SSVEP`
Canonical	`Kim2025`
Importable as	`NM000127`, `Kim2025_SSVEP`, `Kim2025`
Year	2019
Authors	Heegyu Kim, Kyungho Won, Minkyu Ahn, Sung Chan Jun
License	CC BY 4.0
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: 40
Recordings: 240
Tasks: 1

Channels & sampling rate

Channels: 31
Sampling rate (Hz): 1024.0
Duration (hours): 18.927018229166663

API Reference#

Use the NM000127 class to access this dataset programmatically.

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

Bases: EEGDashDataset

Kim2025 – 40-class beta-range SSVEP speller dataset

Study:: nm000127 (NeMAR)
Author (year):: Kim2025_SSVEP
Canonical:: Kim2025

Also importable as: NM000127, Kim2025_SSVEP, Kim2025.

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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None