EEGdash›NeMAR›NM000184

Iss. 184 · 15 subjects · 45 recordings · CC-BY-4.0

Dataset Brief · BCI Competition 2020 Track 5 — ERP during walking (scalp, ear…

NM000184: eeg dataset, 15 subjects#

BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)

Access recordings and metadata through EEGDash.

Citation: Ji-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, Young-Seok Kweon, Jose del R. Millan, Klaus-Robert Muller, Seong-Whan Lee (20). BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU). 10.82901/nemar.nm000184

Modality: eeg Subjects: 15 Recordings: 45 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

15-participant EEG dataset — BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU).

Data & curation Ji-Hoon Jeong · Jeong-Hyun Cho · Young-Eun Lee · Seo-Hyun Lee · Gi-Hwan Shin · Young-Seok Kweon · …
Senior author Seong-Whan Lee
Year 20 · Distributed via NeMAR

EEG · 46 ch100 HzBIDS 1.9.0Task · p300

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000184

dataset = NM000184(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 = NM000184(cache_dir="./data", subject="01")

Advanced query

dataset = NM000184(
    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{nm000184,
  title = {BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)},
  author = {Ji-Hoon Jeong and Jeong-Hyun Cho and Young-Eun Lee and Seo-Hyun Lee and Gi-Hwan Shin and Young-Seok Kweon and Jose del R. Millan and Klaus-Robert Muller and Seong-Whan Lee},
  doi = {10.82901/nemar.nm000184},
  url = {https://doi.org/10.82901/nemar.nm000184},
}

§ 02Study · The README

About This Dataset#

This dataset comprises EEG recordings from 15 healthy participants performing a visual P300 oddball task while walking on a treadmill at 1.6 m/s. The study includes simultaneous scalp-EEG (46 channels), ear-EEG (14 channels, 7 per ear), electrooculography (4 channels), and inertial measurement unit recordings (6 channels: 3-axis accelerometer and gyroscope) sampled at 100 Hz. Data were collected as part of BCI Competition 2020 Track 5 to evaluate brain-computer interface performance under ambulatory conditions, with trials temporally divided into training, validation, and test sets.

Participants performed a visual oddball P300 paradigm with target (‘OOO’) and non-target (‘XXX’) stimuli (target ratio 0.2) while walking on a treadmill at 1.6 m/s. EEG was recorded from 46 scalp channels (standard 10-20 montage) plus 14 ear-EEG channels (7 per ear), 4 EOG channels, and 6 IMU channels (3-axis accelerometer and gyroscope) at 100 Hz sampling rate. Data were epoched from -190 ms to +800 ms around stimulus onset.

DOI

BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000184-blue)](https://doi.org/10.82901/nemar.nm000184) # BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU) ## Overview This dataset comprises EEG recordings from 15 healthy participants performing a visual P300 oddball task while walking on a treadmill at 1.6 m/s. The study includes simultaneous scalp-EEG (46 channels), ear-EEG (14 channels, 7 per ear), electrooculography (4 channels), and inertial measurement unit recordings (6 channels: 3-axis accelerometer and gyroscope) sampled at 100 Hz. Data were collected as part of BCI Competition 2020 Track 5 to evaluate brain-computer interface performance under ambulatory conditions, with trials temporally divided into training, validation, and test sets. ## Dataset Summary | Property | Value | |---|—| | Subjects | 15 | | Channels | 46 | | Classes | 2 | | Trial length | 1 s | | Sampling frequency | 100 Hz | | Sessions | 1 | | Total trials | 4500 | | Paradigm | P300 | ## Data Collection Methods Participants performed a visual oddball P300 paradigm with target (‘OOO’) and non-target (‘XXX’) stimuli (target ratio 0.2) while walking on a treadmill at 1.6 m/s. EEG was recorded from 46 scalp channels (standard 10-20 montage) plus 14 ear-EEG channels (7 per ear), 4 EOG channels, and 6 IMU channels (3-axis accelerometer and gyroscope) at 100 Hz sampling rate. Data were epoched from -190 ms to +800 ms around stimulus onset. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import BCIComp2020WalkingERP from moabb.paradigms import P300 paradigm = P300() dataset = BCIComp2020WalkingERP() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.BCIComp2020WalkingERP.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.3389/fnhum.2022.898300](https://doi.org/10.3389/fnhum.2022.898300) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC-BY-4.0

Authors:

Ji-Hoon Jeong
Jeong-Hyun Cho
Young-Eun Lee
Seo-Hyun Lee
Gi-Hwan Shin
… and 4 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000184

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 46 ch (n=45 recordings)

Sampling frequencies: 100.0 Hz (n=45 recordings)

Total recording duration: 2 h 29 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 46 ch · EEG · 100 Hz · 15 subjects, 45 recordings

Live trace viewer — sub-14 · ses-0 · task-p300 · run-1

Showing one representative recording out of 15 subjects and 45 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 · 32 sensors — 32 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000184(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000184

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000184(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)

Study:: nm000184 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000184.

Modality: eeg; Subject type: Unknown. Subjects: 15; recordings: 45; 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/nm000184 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000184 DOI: https://doi.org/10.82901/nemar.nm000184

Examples

>>> from eegdash.dataset import NM000184
>>> dataset = NM000184(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 — NM000184.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 nm000184 to reproduce the tutorial on this dataset.

Citation

Ji-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, … (20). BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU). 10.82901/nemar.nm000184

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000184.

Related & sibling datasets

ON006563EEG · 12 subj ON004661EEG · 17 subj ON006695EEG · 19 subj ON003626EEG · 10 subj ON004995EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000184

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000184`
Title	BCI Competition 2020 Track 5 — ERP during walking (scalp, ear-EEG, and IMU)
Author (year)	—
Canonical	—
Importable as	`NM000184`
Year	20
Authors	Ji-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, Young-Seok Kweon, Jose del R. Millan, Klaus-Robert Muller, Seong-Whan Lee
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000184
Source links	OpenNeuro \| NeMAR \| Source URL

NM000184: eeg dataset, 15 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#