NM000146: eeg dataset, 10 subjects#

Motor Imagery dataset from Weibo et al 2014

Access recordings and metadata through EEGDash.

Citation: Weibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Lixin Zhang, Peng Zhou, Feng He, Dong Ming (2014). Motor Imagery dataset from Weibo et al 2014. 10.82901/nemar.nm000146

Modality: eeg Subjects: 10 Recordings: 10 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000146

dataset = NM000146(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 = NM000146(cache_dir="./data", subject="01")

Advanced query

dataset = NM000146(
    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{nm000146,
  title = {Motor Imagery dataset from Weibo et al 2014},
  author = {Weibo Yi and Shuang Qiu and Kun Wang and Hongzhi Qi and Lixin Zhang and Peng Zhou and Feng He and Dong Ming},
  doi = {10.82901/nemar.nm000146},
  url = {https://doi.org/10.82901/nemar.nm000146},
}

About This Dataset#

DOI

Motor Imagery dataset from Weibo et al 2014

Motor Imagery dataset from Weibo et al 2014.

Dataset Overview

Code: Weibo2014
Paradigm: imagery

Dataset Information#

Dataset ID	`NM000146`
Title	Motor Imagery dataset from Weibo et al 2014
Author (year)	`Yi2014`
Canonical	—
Importable as	`NM000146`, `Yi2014`
Year	2014
Authors	Weibo Yi, Shuang Qiu, Kun Wang, Hongzhi Qi, Lixin Zhang, Peng Zhou, Feng He, Dong Ming
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000146
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: 10
Recordings: 10
Tasks: 1

Channels & sampling rate

Channels: 60
Sampling rate (Hz): 200.0
Duration (hours): 13.080541666666663

Electrode Layout#

Electrode layout — EEG · 60 sensors — 60 channels

Dataset Statistics#

Age distribution (n=10, range 24–24 yr)

20

Channel counts: 60 ch (n=10 recordings)

Sampling frequencies: 200.0 Hz (n=10 recordings)

Total recording duration: 13 h 4 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 NM000146 class to access this dataset programmatically.

class eegdash.dataset.NM000146(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Motor Imagery dataset from Weibo et al 2014

Study:: nm000146 (NeMAR)
Author (year):: Yi2014
Canonical:: —

Also importable as: NM000146, Yi2014.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 10; recordings: 10; 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/nm000146 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000146 DOI: https://doi.org/10.82901/nemar.nm000146

Examples

>>> from eegdash.dataset import NM000146
>>> dataset = NM000146(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)