DS004447: eeg dataset, 22 subjects#

The BMI-HDEEG dataset 3

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 22 Recordings: 418 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

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

Dataset Information#

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

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: 22
Recordings: 418
Tasks: 1

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0
Duration (hours): 23.55436055555556

Electrode Layout#

Electrode layout — EEG · 129 sensors — 129 channels

Dataset Statistics#

Age distribution (n=30, range 18–27 yr)

152025

Sex distribution

5

25

Female Male Total: 30

Channel counts: 129 ch (n=418 recordings)

Sampling frequencies: 1000.0 Hz (n=418 recordings)

Total recording duration: 23 h 33 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS004447 class to access this dataset programmatically.

class eegdash.dataset.DS004447(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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)