EEGdash›NeMAR›NM000328

Iss. 328 · 60 subjects · 60 recordings · CC-BY-4.0

Dataset Brief · MILimbEEG

NM000328: eeg dataset, 60 subjects#

MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG)

Access recordings and metadata through EEGDash.

Citation: Victor Asanza, Daniel Montoya, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordonez, Kleber Gonzalez (2023). MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG). 10.82901/nemar.nm000328

Modality: eeg Subjects: 60 Recordings: 60 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

60-participant EEG dataset — MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG).

Data & curation Victor Asanza · Daniel Montoya · Leandro L. Lorente-Leyva · Diego H. Peluffo-Ordonez · Kleber Gonzalez
Senior author Kleber Gonzalez
Year 2023 · Distributed via NeMAR

EEG · 16 ch125 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 NM000328

dataset = NM000328(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 = NM000328(cache_dir="./data", subject="01")

Advanced query

dataset = NM000328(
    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{nm000328,
  title = {MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG)},
  author = {Victor Asanza and Daniel Montoya and Leandro L. Lorente-Leyva and Diego H. Peluffo-Ordonez and Kleber Gonzalez},
  doi = {10.82901/nemar.nm000328},
  url = {https://doi.org/10.82901/nemar.nm000328},
}

§ 02Study · The README

About This Dataset#

Motor and motor-imagery limb EEG dataset (MILimbEEG) [1]_ [2]_.

Code: MILimbEEG

Paradigm: imagery DOI: 10.1016/j.dib.2023.109540 Subjects: 60 Sessions per subject: 1 Events: beo=1, clh=2, crh=3, dlf=4, plf=5, drf=6, prf=7, rest=8 Trial interval: [0.0, 3.992] s File format: CSV Contributing labs: ESPOL, Hospital General Luis Vernaza

DOI

MILimbEEG

Acquisition

Sampling rate: 125.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: FC5, F3, Fz, F4, FC6, FC1, FC2, Cz, T7, CP5, C3, CP1, CP2, C4, CP6, T8

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000328-blue)](https://doi.org/10.82901/nemar.nm000328) MILimbEEG ========= Motor and motor-imagery limb EEG dataset (MILimbEEG) [1]_ [2]_. Dataset Overview —————-

Code: MILimbEEG Paradigm: imagery DOI: 10.1016/j.dib.2023.109540 Subjects: 60 Sessions per subject: 1 Events: beo=1, clh=2, crh=3, dlf=4, plf=5, drf=6, prf=7, rest=8 Trial interval: [0.0, 3.992] s File format: CSV Contributing labs: ESPOL, Hospital General Luis Vernaza

Acquisition#

Sampling rate: 125.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: FC5, F3, Fz, F4, FC6, FC1, FC2, Cz, T7, CP5, C3, CP1, CP2, C4, CP6, T8 Montage: 10-10 Hardware: OpenBCI Cyton+Daisy Reference: neutral ear (monopolar) Sensor type: dry Line frequency: 60.0 Hz Electrode type: dry

Participants#

Number of subjects: 60 Health status: mixed Clinical population: mostly healthy; 2 amputees (both upper limbs; right lower limb below the knee), 1 hydrocephalus after ventricular infarct, 18 post-COVID-19 Age: mean=36.0 Gender distribution: female=31, male=29 Handedness: {‘right’: 57, ‘left’: 3}

Experimental Protocol#

Paradigm: imagery Number of classes: 8 Class labels: beo, clh, crh, dlf, plf, drf, prf, rest Trial duration: 4.0 s Study design: Upper- and lower-limb motor execution and motor imagery. Per repetition, participants first performed then imagined hand closing (left/right) and foot flexion (dorsal/plantar, left/right), with a baseline-eyes-open and rest periods. Feedback type: none Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: offline

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser rest

├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Rest

beo
├─ Sensory-event └─ Label/beo

clh
├─ Sensory-event └─ Label/clh

crh
├─ Sensory-event └─ Label/crh

dlf
├─ Sensory-event └─ Label/dlf

plf
├─ Sensory-event └─ Label/plf

drf
├─ Sensory-event └─ Label/drf

prf
├─ Sensory-event └─ Label/prf

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: beo, clh, crh, dlf, plf, drf, prf, rest Imagery duration: 4.0 s

Data Structure#

Trials context: Per repetition and per activity type (execution/imagery): 1 baseline-eyes-open, 5 trials each of 6 limb movements, and 31 rest trials (62 files); only the imagery files are loaded here.

Preprocessing#

Data state: raw Preprocessing applied: True Steps: hardware band-pass 5-50 Hz, 60 Hz notch Highpass filter: 5.0 Hz Lowpass filter: 50.0 Hz Bandpass filter: [5.0, 50.0]

Tags#

Pathology: mixed Modality: Motor Type: Motor Imagery

Documentation#

Description: Over 8,680 four-second EEG recordings from 60 volunteers performing and imagining upper- and lower-limb movements, recorded with a 16-channel OpenBCI Cyton+Daisy at 125 Hz. DOI: 10.1016/j.dib.2023.109540 License: CC-BY-4.0 Investigators: Victor Asanza, Daniel Montoya, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordonez, Kleber Gonzalez Institution: Escuela Superior Politecnica del Litoral (ESPOL) Country: EC Repository: Mendeley Data Data URL: https://doi.org/10.17632/x8psbz3f6x.2 Publication year: 2023 Keywords: motor imagery, motor execution, EEG, brain-computer interface, upper limb, lower limb, OpenBCI

References#

Asanza, V., Montoya, D., Lorente-Leyva, L. L., Peluffo-Ordonez, D. H., & Gonzalez, K. (2023). MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks. Data in Brief, 50, 109540. DOI: https://doi.org/10.1016/j.dib.2023.109540 Asanza, V., Montoya, D., Lorente-Leyva, L. L., Peluffo-Ordonez, D. H., & Gonzalez, K. (2022). MILimbEEG. Mendeley Data, V2. DOI: https://doi.org/10.17632/x8psbz3f6x.2 Notes .. versionadded:: 1.2.1 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference. ## Ethics Approved by the Institutional Review Board (IRB) of the Smart Data Analysis Systems Group (SDAS Group) (meeting minutes IBR-SG-2022-001). All procedures were in accordance with the ethical standards of the institutional/national research committee and with the 1964 Declaration of Helsinki, and informed consent was obtained from all participants (Asanza et al. 2023, Data in Brief 49:109540, DOI 10.1016/j.dib.2023.109540). Verbatim from the source: > All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards, informed consent was obtained from all individual participants involved in this study … An application form for ethics approval alongside with the corresponding supporting documents was timely submitted by the authors and subsequently approved by the Institutional Review Board (IRB) of the Smart Data Analysis Systems Group (Meeting minutes IBR-SG-2022-001). Source: cached paper .paper-audit/MILimbEEG/paper-10_1016_j_dib_2023_109540.txt (Asanza et al. 2023, Data in Brief 49:109540, DOI 10.1016/j.dib.2023.109540).

License: CC-BY-4.0

Authors:

Victor Asanza
Daniel Montoya
Leandro L. Lorente-Leyva
Diego H. Peluffo-Ordonez
Kleber Gonzalez

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000328

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=60, range 36–36 yr, mean 36.0 yr)

35

Other · 60

Channel counts: 16 ch (n=60 recordings)

Sampling frequencies: 125.0 Hz (n=60 recordings)

Total recording duration: 4 h 7 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 125 Hz · 60 subjects, 60 recordings

Live trace viewer — sub-47 · ses-0 · task-imagery · run-0

Showing one representative recording out of 60 subjects and 60 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 · 16 sensors — 16 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.NM000328(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000328

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

class eegdash.dataset.NM000328(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG)

Study:: nm000328 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000328.

Modality: eeg; Subject type: Unknown. Subjects: 60; recordings: 60; 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/nm000328 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000328 DOI: https://doi.org/10.82901/nemar.nm000328

Examples

>>> from eegdash.dataset import NM000328
>>> dataset = NM000328(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 — NM000328.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 nm000328 to reproduce the tutorial on this dataset.

Citation

Victor Asanza, Daniel Montoya, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordonez, Kleber Gonzalez (2023). MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG). 10.82901/nemar.nm000328

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000328.

Related & sibling datasets

NM000327EEG · 55 subj NM000280EEG · 47 subj NM000334EEG · 41 subj NM000296EEG · 30 subj NM000314EEG · 10 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.nm000328

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000328`
Title	MILimbEEG: Motor and motor-imagery limb EEG dataset (MILimbEEG)
Author (year)	—
Canonical	—
Importable as	`NM000328`
Year	2023
Authors	Victor Asanza, Daniel Montoya, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordonez, Kleber Gonzalez
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000328
Source links	OpenNeuro \| NeMAR \| Source URL

NM000328: eeg dataset, 60 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#