NM000142: eeg dataset, 6 subjects#

Ear-EEG motor execution dataset from Wu et al 2020

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 6 Recordings: 13 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

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},
}

About This Dataset#

DOI

Ear-EEG motor execution dataset from Wu et al 2020

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

Dataset Overview

  • Code: Wu2020

  • Paradigm: imagery

View full README

DOI

Ear-EEG motor execution dataset from Wu et al 2020

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

Dataset Overview

  • Code: Wu2020

  • Paradigm: imagery

  • DOI: 10.1088/1741-2552/abc1b6

  • Subjects: 6

  • Sessions per subject: 1

  • Events: left_hand=1, right_hand=2

  • Trial interval: [0, 4] s

  • File format: Curry

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 122

  • Channel types: eeg=122, misc=10

  • Montage: standard_1005

  • Hardware: Neuroscan SynAmps2

  • Reference: scalp REF

  • Ground: scalp GRD

  • Sensor type: Ag/AgCl

  • Line frequency: 50.0 Hz

  • Online filters: {‘bandpass’: [0.5, 100]}

Participants

  • Number of subjects: 6

  • Health status: healthy

  • Age: mean=25.0, min=22.0, max=28.0

  • Gender distribution: female=4, male=2

  • Handedness: right-handed

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 2

  • Class labels: left_hand, right_hand

  • Trial duration: 4.0 s

  • Study design: Motor execution (fist clenching) with simultaneous scalp and ear-EEG recording

  • Feedback type: none

  • Stimulus type: arrow cues

  • Stimulus modalities: visual, auditory

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: offline

HED Event Annotations

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

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

Dataset Information#

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},
}

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: 6

  • Recordings: 13

  • Tasks: 1

Channels & sampling rate

  • Channels: 122

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 4.0056075

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 4.9 GB

  • File count: 13

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: 10.82901/nemar.nm000142

Provenance

Electrode Layout#

Electrode layout — EEG · 122 sensors — 122 channels

Dataset Statistics#

Age distribution (n=6, range 25–25 yr)

25

Channel counts: 122 ch (n=13 recordings)

Sampling frequencies: 1000.0 Hz (n=13 recordings)

Total recording duration: 4 h 0 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.

NEMAR pipeline statistics — NM000142
HED event descriptors word cloud HED event descriptors word cloud — NM000142

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 NM000142 class to access this dataset programmatically.

class eegdash.dataset.NM000142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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.

See Also#