EEGdash›NeMAR›NM000233

Iss. 233 · 14 subjects · 42 recordings · CC-BY-4.0

Dataset Brief · BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

NM000233: eeg dataset, 14 subjects#

BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

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 4 — Upper-limb grasping motor imagery. 10.82901/nemar.nm000233

Modality: eeg Subjects: 14 Recordings: 42 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

14-participant EEG dataset — BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery.

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 · 60 ch250 HzBIDS 1.9.0Task · imagery3 sessions

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 NM000233

dataset = NM000233(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 = NM000233(cache_dir="./data", subject="01")

Advanced query

dataset = NM000233(
    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{nm000233,
  title = {BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery},
  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.nm000233},
  url = {https://doi.org/10.82901/nemar.nm000233},
}

§ 02Study · The README

About This Dataset#

BCIComp2020UpperLimb is a preprocessed derivative EEG dataset from BCI Competition 2020 Track 4 comprising motor imagery recordings of three grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset contains 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session). This is a processed version of the original BCI Competition 2020 data, designed to evaluate session-to-session transfer learning in brain-computer interface applications. Original 10 s trials (3 s rest, 3 s visual cue, 4 s motor imagery) have been preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4 s motor imagery window extracted for analysis.

EEG data were acquired using a BrainAmp system (BrainProducts GmbH) with 60 channels at 250 Hz sampling rate, referenced to FCz with Fpz as ground. Subjects performed cue-based motor imagery of three right-arm grasping tasks in three sessions separated by 7 days. Each trial consisted of a 3 s rest period, 3 s visual cue, and 4 s motor imagery window. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching. The 4 s motor imagery window (corresponding to seconds 6-10 of each original 10 s trial) was extracted by the loader for analysis.

DOI

BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

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.nm000233-blue)](https://doi.org/10.82901/nemar.nm000233) # BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery ## Overview BCIComp2020UpperLimb is a preprocessed derivative EEG dataset from BCI Competition 2020 Track 4 comprising motor imagery recordings of three grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. The dataset contains 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session). This is a processed version of the original BCI Competition 2020 data, designed to evaluate session-to-session transfer learning in brain-computer interface applications. Original 10 s trials (3 s rest, 3 s visual cue, 4 s motor imagery) have been preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4 s motor imagery window extracted for analysis. ## Dataset Summary | Property | Value | |---|—| | Subjects | 15 | | Channels | 60 | | Classes | 3 | | Trial length | 4 s | | Sampling frequency | 250 Hz | | Sessions | 3 | | Total trials | 6750 | | Paradigm | MotorImagery | ## Data Collection Methods EEG data were acquired using a BrainAmp system (BrainProducts GmbH) with 60 channels at 250 Hz sampling rate, referenced to FCz with Fpz as ground. Subjects performed cue-based motor imagery of three right-arm grasping tasks in three sessions separated by 7 days. Each trial consisted of a 3 s rest period, 3 s visual cue, and 4 s motor imagery window. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching. The 4 s motor imagery window (corresponding to seconds 6-10 of each original 10 s trial) was extracted by the loader for analysis. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import BCIComp2020UpperLimb from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = BCIComp2020UpperLimb() 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.BCIComp2020UpperLimb.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.nm000233

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 60 ch (n=42 recordings)

Sampling frequencies: 250.0 Hz (n=42 recordings)

Total recording duration: 7 h 41 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 ch · EEG · 250 Hz · 14 subjects, 42 recordings

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

Showing one representative recording out of 14 subjects and 42 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 · 60 sensors — 60 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.NM000233(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000233

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

class eegdash.dataset.NM000233(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery

Study:: nm000233 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000233.

Modality: eeg; Subject type: Unknown. Subjects: 14; recordings: 42; 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/nm000233 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000233 DOI: https://doi.org/10.82901/nemar.nm000233

Examples

>>> from eegdash.dataset import NM000233
>>> dataset = NM000233(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 — NM000233.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 nm000233 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 4 — Upper-limb grasping motor imagery. 10.82901/nemar.nm000233

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000233.

Related & sibling datasets

NM000113EEG · 15 subj ON003061EEG · 13 subj ON007006EEG · 10 subj ON006563EEG · 12 subj ON004843EEG · 14 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.nm000233

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000233`
Title	BCI Competition 2020 Track 4 — Upper-limb grasping motor imagery
Author (year)	—
Canonical	—
Importable as	`NM000233`
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.nm000233
Source links	OpenNeuro \| NeMAR \| Source URL

NM000233: eeg dataset, 14 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#