EEGdash›NeMAR›NM000178

Iss. 178 · 45 subjects · 45 recordings · CC-BY-4.0

Dataset Brief · BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset

NM000178: eeg dataset, 45 subjects#

BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset

Access recordings and metadata through EEGDash.

Citation: Andreas Schwarz, Carlos Escolano, Luis Montesano, Gernot R. Müller-Putz (20). BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset. 10.82901/nemar.nm000178

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

Metadata: Complete (100%)

45-participant EEG dataset — BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset.

Data & curation Andreas Schwarz · Carlos Escolano · Luis Montesano · Gernot R. Müller-Putz
Senior author Gernot R. Müller-Putz
Year 20 · Distributed via NeMAR
Funding Horizon 2020 Project MoreGrasp

EEG · 32 (15), 12 (15), 58 (15) ch256 HzBIDS 1.9.0Task · imagery

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 NM000178

dataset = NM000178(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 = NM000178(cache_dir="./data", subject="01")

Advanced query

dataset = NM000178(
    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{nm000178,
  title = {BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset},
  author = {Andreas Schwarz and Carlos Escolano and Luis Montesano and Gernot R. Müller-Putz},
  doi = {10.82901/nemar.nm000178},
  url = {https://doi.org/10.82901/nemar.nm000178},
}

§ 02Study · The README

About This Dataset#

This dataset comprises EEG recordings from 45 healthy participants performing self-initiated reach-and-grasp motor imagery tasks using three different recording systems: gel-based laboratory equipment (g.tec USBamp/g.Ladybird), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded at 256 Hz from 58 EEG channels plus 6 EOG channels (64 total channels). The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications.

EEG data were acquired at 256 Hz sampling rate using 58 EEG channels and 6 EOG channels (64 total) with a 5% grid system montage. Three recording systems were employed: gel-based active electrodes (g.tec USBamp/g.Ladybird, reference: right earlobe, ground: AFz), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Online filtering included 8th order Chebyshev filter (0.01-100 Hz) and 50 Hz notch filter. Participants performed 80 self-initiated reach-and-grasp trials each for palmar grasp (toward glass) and lateral grasp (toward spoon) with 2-second fixation period, 1-2 second hold, and 4-second inter-trial interval. Offline preprocessing included 4th order Butterworth bandpass filtering (0.3-60 Hz), extended infomax ICA for artifact removal (applied to gel-based and water-based systems; not applied to dry-electrode recordings due to unfavorable channel count), amplitude thresholding (>125 µV), and abnormal joint probability/kurtosis rejection (4 SD threshold).

DOI

BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset

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.nm000178-blue)](https://doi.org/10.82901/nemar.nm000178) # BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset ## Overview This dataset comprises EEG recordings from 45 healthy participants performing self-initiated reach-and-grasp motor imagery tasks using three different recording systems: gel-based laboratory equipment (g.tec USBamp/g.Ladybird), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Participants executed palmar and lateral grasp actions toward objects while EEG signals were recorded at 256 Hz from 58 EEG channels plus 6 EOG channels (64 total channels). The study investigates the feasibility of decoding natural reach-and-grasp neural correlates across different EEG acquisition modalities for brain-computer interface applications. ## Dataset Summary | Property | Value | |---|—| | Subjects | 15 | | Channels | 11–64 | | Classes | 3 | | Trial length | 5 s | | Sampling frequency | 256 Hz | | Sessions | 3 | | Total trials | 7200 | | Paradigm | MotorImagery | ## Data Collection Methods EEG data were acquired at 256 Hz sampling rate using 58 EEG channels and 6 EOG channels (64 total) with a 5% grid system montage. Three recording systems were employed: gel-based active electrodes (g.tec USBamp/g.Ladybird, reference: right earlobe, ground: AFz), water-based mobile EEG (EEG-Versatile), and dry-electrode mobile EEG (EEG-Hero). Online filtering included 8th order Chebyshev filter (0.01-100 Hz) and 50 Hz notch filter. Participants performed 80 self-initiated reach-and-grasp trials each for palmar grasp (toward glass) and lateral grasp (toward spoon) with 2-second fixation period, 1-2 second hold, and 4-second inter-trial interval. Offline preprocessing included 4th order Butterworth bandpass filtering (0.3-60 Hz), extended infomax ICA for artifact removal (applied to gel-based and water-based systems; not applied to dry-electrode recordings due to unfavorable channel count), amplitude thresholding (>125 µV), and abnormal joint probability/kurtosis rejection (4 SD threshold). ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import BNCI2020_001 from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = BNCI2020_001() 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.BNCI2020_001.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:

Andreas Schwarz
Carlos Escolano
Luis Montesano
Gernot R. Müller-Putz

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000178

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

123258

Sampling frequencies: 256.0 Hz (n=45 recordings)

Total recording duration: 36 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 (15), 12 (15), 58 (15) ch · EEG · 256 Hz · 45 subjects, 45 recordings

Live trace viewer — sub-17 · ses-0 · task-imagery · run-0

Showing one representative recording out of 45 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.NM000178(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000178

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

class eegdash.dataset.NM000178(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset

Study:: nm000178 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000178.

Modality: eeg; Subject type: Unknown. Subjects: 45; 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/nm000178 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000178 DOI: https://doi.org/10.82901/nemar.nm000178

Examples

>>> from eegdash.dataset import NM000178
>>> dataset = NM000178(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 — NM000178.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 nm000178 to reproduce the tutorial on this dataset.

Citation

Andreas Schwarz, Carlos Escolano, Luis Montesano, Gernot R. Müller-Putz (20). BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset. 10.82901/nemar.nm000178

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000178.

Related & sibling datasets

ON007427EEG · 44 subj ON006554EEG · 47 subj ON006648EEG · 47 subj ON007431EEG · 47 subj ON007454EEG · 42 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.nm000178

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000178`
Title	BNCI 2020-001 Reach-and-Grasp Electrode Comparison EEG dataset
Author (year)	—
Canonical	—
Importable as	`NM000178`
Year	20
Authors	Andreas Schwarz, Carlos Escolano, Luis Montesano, Gernot R. Müller-Putz
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000178
Source links	OpenNeuro \| NeMAR \| Source URL

NM000178: eeg dataset, 45 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#