NM000311: eeg dataset, 25 subjects#

Name: Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)
Published: 2020
License: CC0-1.0

Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

Access recordings and metadata through EEGDash.

Citation: Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee (2020). Multimodal upper-limb MI/ME EEG (Jeong et al. 2020). 10.82901/nemar.nm000311

Modality: eeg Subjects: 25 Recordings: 213 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000311

dataset = NM000311(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 = NM000311(cache_dir="./data", subject="01")

Advanced query

dataset = NM000311(
    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{nm000311,
  title = {Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)},
  author = {Ji-Hoon Jeong and Jeong-Hyun Cho and Kyung-Hwan Shim and Byoung-Hee Kwon and Byeong-Hoo Lee and Do-Yeun Lee and Dae-Hyeok Lee and Seong-Whan Lee},
  doi = {10.82901/nemar.nm000311},
  url = {https://doi.org/10.82901/nemar.nm000311},
}

About This Dataset#

DOI

Jeong2020

Multimodal MI+ME dataset from Jeong et al 2020.

Dataset Overview

Code: Jeong2020 Paradigm: imagery

Dataset Information#

Dataset ID	`NM000311`
Title	Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)
Author (year)	`Jeong2020`
Canonical	—
Importable as	`NM000311`, `Jeong2020`
Year	2020
Authors	Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000311
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: 25
Recordings: 213
Tasks: 1

Channels & sampling rate

Channels: 71
Sampling rate (Hz): 1000.0
Duration (hours): 124.0643847222222

API Reference#

Use the NM000311 class to access this dataset programmatically.

class eegdash.dataset.NM000311(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

Study:: nm000311 (NeMAR)
Author (year):: Jeong2020
Canonical:: —

Also importable as: NM000311, Jeong2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 213; 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/nm000311 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000311 DOI: https://doi.org/10.82901/nemar.nm000311

Examples

>>> from eegdash.dataset import NM000311
>>> dataset = NM000311(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