EEGdash›NeMAR›ON004362

Iss. 4362 · 109 subjects · 1526 recordings · CC0

Dataset Brief · EEG Motor Movement/Imagery Dataset

ON004362: eeg dataset, 109 subjects#

EEG Motor Movement/Imagery Dataset

Citation: Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger, Niels Birbaumer, Jonathan R Wolpaw (—). EEG Motor Movement/Imagery Dataset. 10.82901/nemar.on004362

109-participant EEG dataset — EEG Motor Movement/Imagery Dataset.

Data & curation Gerwin Schalk · Dennis J McFarland · Thilo Hinterberger · Niels Birbaumer · Jonathan R Wolpaw

EEG · 64 ch160 Hz · mixedBIDS 1.8.0Task · motion

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 ON004362

dataset = ON004362(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 = ON004362(cache_dir="./data", subject="01")

Advanced query

dataset = ON004362(
    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{on004362,
  title = {EEG Motor Movement/Imagery Dataset},
  author = {Gerwin Schalk and Dennis J McFarland and Thilo Hinterberger and Niels Birbaumer and Jonathan R Wolpaw},
  doi = {10.82901/nemar.on004362},
  url = {https://doi.org/10.82901/nemar.on004362},
}

§ 02Study · The README

About This Dataset#

This data set was originally created and contributed to PhysioBank by Gerwin Schalk (schalk at wadsworth dot org) and his colleagues at the BCI R&D Program, Wadsworth Center, New York State Department of Health, Albany, NY. W.A. Sarnacki collected the data. Aditya Joshi compiled the dataset and prepared the documentation. D.J. McFarland and J.R. Wolpaw were responsible for experimental design and project oversight, respectively. This work was supported by grants from NIH/NIBIB ((EB006356 (GS) and EB00856 (JRW and GS)).

To access the initial publication of this dataset, please visit this link to PhysioBank: https://physionet.org/content/eegmmidb/1.0.0/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers, as described below.

DOI ##Acknowledgements

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org). Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks: [Task 1] A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes. [Task 2] A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes. [Task 3] A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes. [Task 4] A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

In summary, the experimental runs were:

View full README

DOI ##Acknowledgements

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org). Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks: [Task 1] A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes. [Task 2] A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes. [Task 3] A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes. [Task 4] A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

In summary, the experimental runs were:

Baseline, eyes open

Baseline, eyes closed

Task 1 (open and close left or right fist)

Task 2 (imagine opening and closing left or right fist)

Task 3 (open and close both fists or both feet)

Task 4 (imagine opening and closing both fists or both feet)

Task 1

Task 2

Task 3

Task 4

Task 1

Task 2

Task 3

Task 4

Each event code includes an event type indicator (T0, T1, or T2) that is concatenated to the Task # it belongs with (i.e TASK1T2). The event type indicators change definition depending on the Task # it is associated with. For example, TASK1T2 would correspond to the onset of real motion in the right fist, while TASK3T2 would correspond to onset of real motion in both feet: [T0] corresponds to rest [T1] corresponds to onset of motion (real or imagined) of: - the left fist (in runs 3, 4, 7, 8, 11, and 12; for Task 1 (real) and Task 2 (imagined)) - both fists (in runs 5, 6, 9, 10, 13, and 14; for Task 3 (real) and Task 4 (imagined))

[T2] corresponds to onset of motion (real or imagined) of: - the right fist (in runs 3, 4, 7, 8, 11, and 12; Task 1 (real) and Task 2 (imagined)) - both feet (in runs 5, 6, 9, 10, 13, and 14; for Task 3 (real) and Task 4 (imagined))

Note: The data files in this dataset were converted into the .set format for EEGLAB. The event codes in the .set files of this dataset will contain the concatenated event codes above for all event files for clarity purposes. The non-converted .edf files along with the accompanying PhysioBank-compatible annotation files for all the runs of each subject can be found in the sourcedata folder. In the non-converted .edf files the event codes will only be shown as T0, T1, and T2 regardless of task type. All the Matlab scripts used for the .set conversion and renaming of event codes of the PhysioBank .edf files can be found in the code folder.

Montage

The EEGs were recorded from 64 electrodes as per the international 10-10 system (excluding electrodes Nz, F9, F10, FT9, FT10, A1, A2, TP9, TP10, P9, and P10), as shown in the figure in the code folder. The numbers below each electrode name indicate the order in which they appear in the records; note that signals in the records are numbered from 0 to 63, while the numbers in the figure range from 1 to 64.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=95, range 19–67 yr, mean 38.8 yr · sex per subject not reported)

15202530354045505565

Sex composition

Female
59
Male
42
F : M ratio
1.40 : 1

58% female · n = 101 subjects with reported sex.

HandednessRight · 88Left · 11

Channel counts: 64 ch (n=1526 recordings)

Sampling frequencies (Hz)

128160

Total recording duration: 48 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 160 Hz · mixed · 109 subjects, 1526 recordings

Live trace viewer — sub-001 · task-motion · run-10

Showing one representative recording out of 109 subjects and 1526 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 · 64 sensors — 64 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.ON004362(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON004362

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

class eegdash.dataset.ON004362(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG Motor Movement/Imagery Dataset

Study:: on004362 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON004362, nan.

Modality: eeg. Subjects: 109; recordings: 1526; 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/on004362 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004362 DOI: https://doi.org/10.82901/nemar.on004362

Examples

>>> from eegdash.dataset import ON004362
>>> dataset = ON004362(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 — ON004362.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 on004362 to reproduce the tutorial on this dataset.

Citation

Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger, Niels Birbaumer, Jonathan R Wolpaw (n.d.). EEG Motor Movement/Imagery Dataset. 10.82901/nemar.on004362

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on004362.

Related & sibling datasets

ON005505EEG · 136 subj ON005506EEG · 150 subj ON005510EEG · 135 subj NM000134EEG · 20 subj NM000166EEG · 95 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.82901/nemar.on004362

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON004362`
Title	EEG Motor Movement/Imagery Dataset
Author (year)	—
Canonical	—
Importable as	`ON004362`
Year	—
Authors	Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger, Niels Birbaumer, Jonathan R Wolpaw
License	CC0
Citation / DOI	10.82901/nemar.on004362
Source links	OpenNeuro \| NeMAR \| Source URL

ON004362: eeg dataset, 109 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#