EEGdash›OpenNeuro›DS004447
Iss. 4447 · 22 subjects · 418 recordings · CC0
Dataset Brief · The BMI-HDEEG dataset 3

DS004447: eeg dataset, 22 subjects#

The BMI-HDEEG dataset 3

Citation: Seitaro Iwama, Masumi Morishige, Yoshikazu Takahashi, Ryotaro Hirose, Midori Kodama, Junichi Ushiba (2023). The BMI-HDEEG dataset 3. 10.18112/openneuro.ds004447.v1.0.1

22-participant EEG dataset — The BMI-HDEEG dataset 3.

Data & curation Seitaro Iwama · Masumi Morishige · Yoshikazu Takahashi · Ryotaro Hirose · Midori Kodama · Junichi Ushiba
Year 2023 · Distributed via OpenNeuro
Funding JST Moonshot R&D JPMJMS2012
EEG · 129 ch1000 HzBIDS 1.7.0Task · smrbmi20 sessionsHealthyVisualMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004447

dataset = DS004447(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004447(cache_dir="./data", subject="01")

Advanced query

dataset = DS004447(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004447,
  title = {The BMI-HDEEG dataset 3},
  author = {Seitaro Iwama and Masumi Morishige and Yoshikazu Takahashi and Ryotaro Hirose and Midori Kodama and Junichi Ushiba},
  doi = {10.18112/openneuro.ds004447.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004447.v1.0.1},
}
§ 02Study · The README

About This Dataset#

Data Descriptor Article

Iwama, S., Morishige, M., Kodama, M. et al. High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing. Sci Data 10, 385 (2023). https://doi.org/10.1038/s41597-023-02260-6 Sample code Junichi-Ushiba-Laboratory/pj-hd-smrbmi

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=22, range 19–27 yr, mean 21.9 yr)

152025
Other · 22

Sex composition

30
subjects
Female
5
Male
25
F : M ratio
0.20 : 1
17% female · n = 30 subjects with reported sex.
HandednessRight · 29Left · 1

Channel counts: 129 ch (n=418 recordings)

Sampling frequencies: 1000.0 Hz (n=418 recordings)

Total recording duration: 23 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 129 ch · EEG · 1000 Hz · 22 subjects, 418 recordings
Live trace viewer — sub-021 · ses-19 · task-smrbmi

Showing one representative recording out of 22 subjects and 418 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 · 129 sensors — 129 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.

NEMAR pipeline statistics — DS004447
HED event descriptors word cloud HED event descriptors word cloud — DS004447
§ 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…
Full dataset metadata table

Dataset ID

DS004447

Title

The BMI-HDEEG dataset 3

Author (year)

Iwama2023_D3

Canonical

—

Importable as

DS004447, Iwama2023_D3

Year

2023

Authors

Seitaro Iwama, Masumi Morishige, Yoshikazu Takahashi, Ryotaro Hirose, Midori Kodama, Junichi Ushiba

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004447.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004447,
  title = {The BMI-HDEEG dataset 3},
  author = {Seitaro Iwama and Masumi Morishige and Yoshikazu Takahashi and Ryotaro Hirose and Midori Kodama and Junichi Ushiba},
  doi = {10.18112/openneuro.ds004447.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004447.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004447(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Iwama2023_D3
Canonical—
Importable asDS004447 · Iwama2023_D3
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004447(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

The BMI-HDEEG dataset 3

Study:

ds004447 (OpenNeuro)

Author (year):

Iwama2023_D3

Canonical:

—

Also importable as: DS004447, Iwama2023_D3.

Modality: eeg. Subjects: 22; recordings: 418; 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/ds004447 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004447 DOI: https://doi.org/10.18112/openneuro.ds004447.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004447
>>> dataset = DS004447(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 FacePre-bundled mirror at EEGDash/ds004447 · pull with datasets.load_dataset("EEGDash/ds004447").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004447.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 ds004447 to reproduce the tutorial on this dataset.

Citation

Seitaro Iwama, Masumi Morishige, Yoshikazu Takahashi, Ryotaro Hirose, Midori Kodama, … (2023). The BMI-HDEEG dataset 3. 10.18112/openneuro.ds004447.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004447.v1.0.1.

BIDS
BIDS 1.7.0
Sidecars
events · channels · electrodes · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#