NM000201: eeg dataset, 24 subjects#

ERP paradigm of the Mobile BCI dataset

Access recordings and metadata through EEGDash.

Citation: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee (2019). ERP paradigm of the Mobile BCI dataset.

Modality: eeg Subjects: 24 Recordings: 113 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000201

dataset = NM000201(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000201(cache_dir="./data", subject="01")

Advanced query

dataset = NM000201(
    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{nm000201,
  title = {ERP paradigm of the Mobile BCI dataset},
  author = {Young-Eun Lee and Gi-Hwan Shin and Minji Lee and Seong-Whan Lee},
}

About This Dataset#

ERP paradigm of the Mobile BCI dataset

ERP paradigm of the Mobile BCI dataset.

Dataset Overview

  • Code: Lee2021Mobile-ERP

  • Paradigm: p300

  • DOI: 10.1038/s41597-021-01094-4

View full README

ERP paradigm of the Mobile BCI dataset

ERP paradigm of the Mobile BCI dataset.

Dataset Overview

  • Code: Lee2021Mobile-ERP

  • Paradigm: p300

  • DOI: 10.1038/s41597-021-01094-4

  • Subjects: 24

  • Sessions per subject: 5

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 1.0] s

  • File format: BrainVision

Acquisition

  • Sampling rate: 100.0 Hz

  • Number of channels: 73

  • Channel types: eeg=73

  • Montage: standard_1005

  • Hardware: BrainAmp (Brain Product GmbH)

  • Reference: FCz

  • Ground: Fpz

  • Sensor type: Ag/AgCl

  • Line frequency: 60.0 Hz

  • Impedance threshold: 50 kOhm

  • Electrode material: Ag/AgCl

  • Auxiliary channels: EOG (4 ch, vertical, horizontal)

Participants

  • Number of subjects: 24

  • Health status: healthy

  • Age: mean=24.5, std=2.9, min=19, max=32

  • Gender distribution: male=14, female=10

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 1.0 s

  • Study design: BCI during motion (standing/walking/running)

  • Stimulus type: visual oddball

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: offline

HED Event Annotations

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

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

BCI Application

  • Environment: mobile

  • Online feedback: False

Tags

  • Pathology: healthy

  • Modality: visual

  • Type: perception

Documentation

  • DOI: 10.1038/s41597-021-01094-4

  • License: CC BY 4.0

  • Investigators: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee

  • Senior author: Seong-Whan Lee

  • Institution: Korea University

  • Country: KR

  • Repository: OSF

  • Data URL: https://osf.io/r7s9b/

  • Publication year: 2021

  • Funding: IITP No. 2017-0-00451; IITP No. 2015-0-00185; IITP No. 2019-0-00079

  • Ethics approval: Institutional Review Board of Korea University, KUIRB-2019-0194-01

  • Keywords: SSVEP, ERP, mobile BCI, ear-EEG, locomotion

References

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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000201

Title

ERP paradigm of the Mobile BCI dataset

Author (year)

Lee2021_ERP

Canonical

—

Importable as

NM000201, Lee2021_ERP

Year

2019

Authors

Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee

License

CC BY 4.0

Citation / DOI

Unknown

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: 24

  • Recordings: 113

  • Tasks: 1

Channels & sampling rate

  • Channels: 48 (108), 73 (5)

  • Sampling rate (Hz): 500.0 (108), 100.0 (5)

  • Duration (hours): 22.13410722222222

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 5.2 GB

  • File count: 113

  • Format: BIDS

License & citation

  • License: CC BY 4.0

  • DOI: —

Provenance

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=24, range 19–32 yr)

15202530

Sex distribution

10
14
Female  Male  Total: 24

Channel counts (ch)

4873

Sampling frequencies (Hz)

100500

Total recording duration: 22 h 8 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 — NM000201
HED event descriptors word cloud HED event descriptors word cloud — NM000201

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 NM000201 class to access this dataset programmatically.

class eegdash.dataset.NM000201(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

ERP paradigm of the Mobile BCI dataset

Study:

nm000201 (NeMAR)

Author (year):

Lee2021_ERP

Canonical:

—

Also importable as: NM000201, Lee2021_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 113; 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/nm000201 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000201

Examples

>>> from eegdash.dataset import NM000201
>>> dataset = NM000201(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#