EEGdash›NeMAR›NM000314

Iss. 314 · 10 subjects · 60 recordings · CC-BY-4.0

Dataset Brief · MartinezPeon2024

NM000314: eeg dataset, 10 subjects#

MartinezPeon2024: Kinesthetic motor imagery at graded force levels

Access recordings and metadata through EEGDash.

Citation: Dulce Martinez-Peon (2024). MartinezPeon2024: Kinesthetic motor imagery at graded force levels. 10.82901/nemar.nm000314

Modality: eeg Subjects: 10 Recordings: 60 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — MartinezPeon2024: Kinesthetic motor imagery at graded force levels.

Data & curation Dulce Martinez-Peon
Senior author Dulce Martinez-Peon
Year 2024 · Distributed via NeMAR

EEG · 14 ch128 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 NM000314

dataset = NM000314(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 = NM000314(cache_dir="./data", subject="01")

Advanced query

dataset = NM000314(
    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{nm000314,
  title = {MartinezPeon2024: Kinesthetic motor imagery at graded force levels},
  author = {Dulce Martinez-Peon},
  doi = {10.82901/nemar.nm000314},
  url = {https://doi.org/10.82901/nemar.nm000314},
}

§ 02Study · The README

About This Dataset#

Kinesthetic motor imagery at graded force levels [1]_.

Code: MartinezPeon2024

Paradigm: imagery DOI: 10.6084/m9.figshare.25773342.v1 Subjects: 10 Sessions per subject: 1 Events: level_10=1, level_40=2, level_70=3 Trial interval: (0, 5) s Runs per session: 6 File format: TXT

DOI

MartinezPeon2024

Acquisition

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4

View full README

DOI

MartinezPeon2024

Acquisition

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 Montage: standard_1020 Hardware: Emotiv EPOC Reference: P3 and P4 (Emotiv EPOC reference locations) Sensor type: wet Line frequency: 60.0 Hz Electrode type: saline

Participants

Number of subjects: 10 Health status: healthy Age: mean=27.8, std=2.78, min=24.0, max=32.0 Gender distribution: male=7, female=3

Experimental Protocol

Paradigm: imagery Number of classes: 3 Class labels: level_10, level_40, level_70 Trial duration: 5.0 s Trials per class: level_10=10, level_40=10, level_70=10 Study design: Kinesthetic motor imagery of a right-hand ball squeeze at 10/40/70% of maximal voluntary contraction. Each 40 s recording carries five KMI cues at 2.9/10.9/18.9/26.9/34.9 s (5 s each); each force level is recorded twice per subject. The force level is encoded in the file name. Feedback type: none Synchronicity: cue-based Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser level_10

     ├─ Sensory-event
     └─ Label/level_10

level_40

     ├─ Sensory-event
     └─ Label/level_40

level_70

├─ Sensory-event
└─ Label/level_70

Tags

Modality: Motor Type: Motor Imagery

Documentation

Description: EEG kinesthetic motor imagery at three graded hand-grip force levels (10/40/70% MVC) from 10 healthy subjects, Emotiv EPOC, 14 channels, 128 Hz. DOI: 10.6084/m9.figshare.25773342.v1 Associated paper DOI: 10.1088/1741-2552/ad5f27 License: CC-BY-4.0 Investigators: Dulce Martinez-Peon Institution: National Technological Institute of Mexico (TecNM) - IT Nuevo Leon; Cinvestav Saltillo Country: MX Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.25773342 Publication year: 2024 Keywords: motor imagery, kinesthetic motor imagery, force level, hand grip, EEG, BCI

References

Martinez-Peon, D. (2024). EEG Kinesthetic motor imagery levels. figshare. Dataset. DOI: https://doi.org/10.6084/m9.figshare.25773342 Martinez-Peon, D., Garcia-Hernandez, N. V., Benavides-Bravo, F. G., & Parra-Vega, V. (2024). Characterization and classification of kinesthetic motor imagery levels. Journal of Neural Engineering, 21(4), 046024. DOI: https://doi.org/10.1088/1741-2552/ad5f27 Notes .. versionadded:: 1.8.0 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

Assessed and approved by the local Ethics Committee of Cinvestav (the research institution). The study was conducted under the principles of the Declaration of Helsinki, and all participants gave written informed consent (Martinez-Peon et al. 2024, J. Neural Eng., DOI 10.1088/1741-2552/ad5f27).

Verbatim from the source:

The study was assessed and approved by the local Ethics Committee of the research institution (Cinvestav) and performed under the Helsinki Declaration. All participants received an explanation of the purpose of the study, filled out the consent form, and performed the study.

Source: cached paper .paper-audit/MartinezPeon2024/paper-10_1088_1741_2552_ad5f27.txt (Martinez-Peon et al. 2024, J. Neural Eng., DOI 10.1088/1741-2552/ad5f27).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000314-blue)](https://doi.org/10.82901/nemar.nm000314) MartinezPeon2024 ================ Kinesthetic motor imagery at graded force levels [1]_. Dataset Overview —————-

Code: MartinezPeon2024 Paradigm: imagery DOI: 10.6084/m9.figshare.25773342.v1 Subjects: 10 Sessions per subject: 1 Events: level_10=1, level_40=2, level_70=3 Trial interval: (0, 5) s Runs per session: 6 File format: TXT

Acquisition#

Sampling rate: 128.0 Hz Number of channels: 14 Channel types: eeg=14 Channel names: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 Montage: standard_1020 Hardware: Emotiv EPOC Reference: P3 and P4 (Emotiv EPOC reference locations) Sensor type: wet Line frequency: 60.0 Hz Electrode type: saline

Participants#

Number of subjects: 10 Health status: healthy Age: mean=27.8, std=2.78, min=24.0, max=32.0 Gender distribution: male=7, female=3

Experimental Protocol#

Paradigm: imagery Number of classes: 3 Class labels: level_10, level_40, level_70 Trial duration: 5.0 s Trials per class: level_10=10, level_40=10, level_70=10 Study design: Kinesthetic motor imagery of a right-hand ball squeeze at 10/40/70% of maximal voluntary contraction. Each 40 s recording carries five KMI cues at 2.9/10.9/18.9/26.9/34.9 s (5 s each); each force level is recorded twice per subject. The force level is encoded in the file name. Feedback type: none Synchronicity: cue-based Mode: offline

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser level_10

├─ Sensory-event └─ Label/level_10

level_40
├─ Sensory-event └─ Label/level_40

level_70
├─ Sensory-event └─ Label/level_70

Tags#

Modality: Motor Type: Motor Imagery

Documentation#

Description: EEG kinesthetic motor imagery at three graded hand-grip force levels (10/40/70% MVC) from 10 healthy subjects, Emotiv EPOC, 14 channels, 128 Hz. DOI: 10.6084/m9.figshare.25773342.v1 Associated paper DOI: 10.1088/1741-2552/ad5f27 License: CC-BY-4.0 Investigators: Dulce Martinez-Peon Institution: National Technological Institute of Mexico (TecNM) - IT Nuevo Leon; Cinvestav Saltillo Country: MX Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.25773342 Publication year: 2024 Keywords: motor imagery, kinesthetic motor imagery, force level, hand grip, EEG, BCI

References#

Martinez-Peon, D. (2024). EEG Kinesthetic motor imagery levels. figshare. Dataset. DOI: https://doi.org/10.6084/m9.figshare.25773342 Martinez-Peon, D., Garcia-Hernandez, N. V., Benavides-Bravo, F. G., & Parra-Vega, V. (2024). Characterization and classification of kinesthetic motor imagery levels. Journal of Neural Engineering, 21(4), 046024. DOI: https://doi.org/10.1088/1741-2552/ad5f27 Notes .. versionadded:: 1.8.0 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 Assessed and approved by the local Ethics Committee of Cinvestav (the research institution). The study was conducted under the principles of the Declaration of Helsinki, and all participants gave written informed consent (Martinez-Peon et al. 2024, J. Neural Eng., DOI 10.1088/1741-2552/ad5f27). Verbatim from the source: > The study was assessed and approved by the local Ethics Committee of the research institution (Cinvestav) and performed under the Helsinki Declaration. All participants received an explanation of the purpose of the study, filled out the consent form, and performed the study. Source: cached paper .paper-audit/MartinezPeon2024/paper-10_1088_1741_2552_ad5f27.txt (Martinez-Peon et al. 2024, J. Neural Eng., DOI 10.1088/1741-2552/ad5f27).

License: CC-BY-4.0

Authors:

Dulce Martinez-Peon

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000314

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 28–28 yr, mean 27.0 yr)

25

Other · 10

Channel counts: 14 ch (n=60 recordings)

Sampling frequencies: 128.0 Hz (n=60 recordings)

Total recording duration: 41 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 14 ch · EEG · 128 Hz · 10 subjects, 60 recordings

Live trace viewer — sub-7 · ses-0 · task-imagery · run-0

Showing one representative recording out of 10 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 · 14 sensors — 14 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.NM000314(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000314

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

class eegdash.dataset.NM000314(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MartinezPeon2024: Kinesthetic motor imagery at graded force levels

Study:: nm000314 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000314.

Modality: eeg; Subject type: Unknown. Subjects: 10; 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/nm000314 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000314 DOI: https://doi.org/10.82901/nemar.nm000314

Examples

>>> from eegdash.dataset import NM000314
>>> dataset = NM000314(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 — NM000314.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 nm000314 to reproduce the tutorial on this dataset.

Citation

Dulce Martinez-Peon (2024). MartinezPeon2024: Kinesthetic motor imagery at graded force levels. 10.82901/nemar.nm000314

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000314.

Related & sibling datasets

NM000315EEG · 10 subj NM000300EEG · 10 subj NM000320EEG · 10 subj NM000291EEG · 14 subj NM000294EEG · 12 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.nm000314

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000314`
Title	MartinezPeon2024: Kinesthetic motor imagery at graded force levels
Author (year)	—
Canonical	—
Importable as	`NM000314`
Year	2024
Authors	Dulce Martinez-Peon
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000314
Source links	OpenNeuro \| NeMAR \| Source URL

NM000314: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

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#