EEGdash›NeMAR›ON003810

Iss. 3810 · 10 subjects · 50 recordings · CC0

Dataset Brief · Motor Imagery vs Rest - Low-Cost EEG System

ON003810: eeg dataset, 10 subjects#

Motor Imagery vs Rest - Low-Cost EEG System

Citation: Victoria Peterson, Catalina Maria Galvan, Hugo Sacha Hernadez, Ruben Spies (—). Motor Imagery vs Rest - Low-Cost EEG System. 10.82901/nemar.on003810

10-participant EEG dataset — Motor Imagery vs Rest - Low-Cost EEG System.

Data & curation Victoria Peterson · Catalina Maria Galvan · Hugo Sacha Hernadez · Ruben Spies

EEG · 15 ch125 HzBIDS 1.1.1Task · MIvsRest

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 ON003810

dataset = ON003810(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 = ON003810(cache_dir="./data", subject="01")

Advanced query

dataset = ON003810(
    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{on003810,
  title = {Motor Imagery vs Rest - Low-Cost EEG System},
  author = {Victoria Peterson and Catalina Maria Galvan and Hugo Sacha Hernadez and Ruben Spies},
  doi = {10.82901/nemar.on003810},
  url = {https://doi.org/10.82901/nemar.on003810},
}

§ 02Study · The README

About This Dataset#

This dataset consists of electroencephalography (EEG) signals adquired with a low-cost consumer-grade device. The 10 participants had no previous BCI experience. The BCI protocol consisted of two conditions, namely the kinesthetic imagination of grasping movement (MI) of the dominant hand and rest/idle condition.Five protocol runs were asked to be performed by the user. The first run, called RUN0, involved real grasping movement in order to better explain the protocol and to help the subject to focus on the sensation of making the movement. The rest of the runs (RUN1-RUN4) were equal, consisting of MI vs.Rest conditions. The EMG signals of the dominant hand was adquired for protocol control. During acquisition, the EEG signals were filtered between 0.5 and 45 Hz with a 3rd order Butterworth bandpass-filter.

DOI

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 20–30 yr, mean 26.1 yr)

202530

Other · 10

Sex composition

Female
4
Male
6
F : M ratio
0.67 : 1

40% female · n = 10 subjects with reported sex.

Channel counts: 15 ch (n=50 recordings)

Sampling frequencies: 125.0 Hz (n=50 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 15 ch · EEG · 125 Hz · 10 subjects, 50 recordings

Live trace viewer — sub-02 · task-MIvsRest · run-0

Showing one representative recording out of 10 subjects and 50 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 · 15 sensors — 15 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.ON003810(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON003810

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

class eegdash.dataset.ON003810(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Motor Imagery vs Rest - Low-Cost EEG System

Study:: on003810 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON003810, nan.

Modality: eeg. Subjects: 10; recordings: 50; 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/on003810 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003810 DOI: https://doi.org/10.82901/nemar.on003810

Examples

>>> from eegdash.dataset import ON003810
>>> dataset = ON003810(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 — ON003810.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 on003810 to reproduce the tutorial on this dataset.

Citation

Victoria Peterson, Catalina Maria Galvan, Hugo Sacha Hernadez, Ruben Spies (n.d.). Motor Imagery vs Rest - Low-Cost EEG System. 10.82901/nemar.on003810

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on003810.

Related & sibling datasets

ON003195EEG · 10 subj ON005028EEG · 11 subj NM000226EEG · 4 subj ON003194EEG · 15 subj ON003800EEG · 13 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.1.1

Sidecars

channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on003810

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON003810`
Title	Motor Imagery vs Rest - Low-Cost EEG System
Author (year)	—
Canonical	—
Importable as	`ON003810`
Year	—
Authors	Victoria Peterson, Catalina Maria Galvan, Hugo Sacha Hernadez, Ruben Spies
License	CC0
Citation / DOI	10.82901/nemar.on003810
Source links	OpenNeuro \| NeMAR \| Source URL

ON003810: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#