EEGdash›NeMAR›NM000142
Iss. 142 · 6 subjects · 13 recordings · CC-BY-4.0
Dataset Brief · Ear-EEG motor execution dataset from Wu et al 2020

NM000142: eeg dataset, 6 subjects#

Ear-EEG motor execution dataset from Wu et al 2020

Citation: Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T.H. Cheung, Rosa H.M. Chan (2020). Ear-EEG motor execution dataset from Wu et al 2020. 10.82901/nemar.nm000142

6-participant EEG dataset — Ear-EEG motor execution dataset from Wu et al 2020.

Data & curation Xiaoli Wu · Wenhui Zhang · Zhibo Fu · Roy T.H. Cheung · Rosa H.M. Chan
Year 2020 · Distributed via NeMAR
EEG · 122 ch1000 HzBIDS 1.9.0Task · imageryHealthyVisualMotor
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 NM000142

dataset = NM000142(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000142(cache_dir="./data", subject="01")

Advanced query

dataset = NM000142(
    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{nm000142,
  title = {Ear-EEG motor execution dataset from Wu et al 2020},
  author = {Xiaoli Wu and Wenhui Zhang and Zhibo Fu and Roy T.H. Cheung and Rosa H.M. Chan},
  doi = {10.82901/nemar.nm000142},
  url = {https://doi.org/10.82901/nemar.nm000142},
}
§ 02Study · The README

About This Dataset#

Ear-EEG motor execution dataset from Wu et al 2020.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

DOI

Ear-EEG motor execution dataset from Wu et al 2020

left_hand
View full README

DOI

Ear-EEG motor execution dataset from Wu et al 2020

left_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right, Hand

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: left_hand, right_hand

Data Structure

  • Trials: 1114

  • Trials context: S1: 240, S2: 160, S3: 160, S4: 80, S5: 234, S6: 240 = 1114

Signal Processing

  • Classifiers: EEGNet

Cross-Validation

  • Evaluation type: within_subject

BCI Application

  • Applications: motor_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Research

Documentation

  • DOI: 10.1088/1741-2552/abc1b6

  • License: CC-BY-4.0

  • Investigators: Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T.H. Cheung, Rosa H.M. Chan

  • Institution: City University of Hong Kong

  • Country: HK

  • Repository: Zenodo

  • Data URL: https://zenodo.org/records/18961128

  • Publication year: 2020

References

Wu, X., Zhang, W., Fu, Z., Cheung, R. T. H., & Chan, R. H. M. (2020). An investigation of in-ear sensing for motor task classification. Journal of Neural Engineering, 17(6), 066029. https://doi.org/10.1088/1741-2552/abc1b6 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.4.3 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=6, range 25–25 yr, mean 25.0 yr)

25
Other · 6

Channel counts: 122 ch (n=13 recordings)

Sampling frequencies: 1000.0 Hz (n=13 recordings)

Total recording duration: 4 h 0 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 122 ch · EEG · 1000 Hz · 6 subjects, 13 recordings
Live trace viewer — sub-6 · ses-0 · task-imagery · run-2

Showing one representative recording out of 6 subjects and 13 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 · 122 sensors — 122 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 — NM000142
HED event descriptors word cloud HED event descriptors word cloud — NM000142
§ 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

NM000142

Title

Ear-EEG motor execution dataset from Wu et al 2020

Author (year)

Wu2020

Canonical

—

Importable as

NM000142, Wu2020

Year

2020

Authors

Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T.H. Cheung, Rosa H.M. Chan

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000142

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000142,
  title = {Ear-EEG motor execution dataset from Wu et al 2020},
  author = {Xiaoli Wu and Wenhui Zhang and Zhibo Fu and Roy T.H. Cheung and Rosa H.M. Chan},
  doi = {10.82901/nemar.nm000142},
  url = {https://doi.org/10.82901/nemar.nm000142},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000142(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Wu2020
Canonical—
Importable asNM000142 · Wu2020
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Ear-EEG motor execution dataset from Wu et al 2020

Study:

nm000142 (NeMAR)

Author (year):

Wu2020

Canonical:

—

Also importable as: NM000142, Wu2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 6; recordings: 13; 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/nm000142 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000142 DOI: https://doi.org/10.82901/nemar.nm000142

Examples

>>> from eegdash.dataset import NM000142
>>> dataset = NM000142(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — NM000142.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 nm000142 to reproduce the tutorial on this dataset.

Citation

Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T.H. Cheung, Rosa H.M. Chan (2020). Ear-EEG motor execution dataset from Wu et al 2020. 10.82901/nemar.nm000142

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000142.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000142
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#