EEGdash›NeMAR›NM000320

Iss. 320 · 10 subjects · 192 recordings · CC-BY-4.0

Dataset Brief · Ortiz2023

NM000320: eeg dataset, 10 subjects#

Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton

Access recordings and metadata through EEGDash.

Citation: Mario Ortiz, Luis de la Ossa, Eduardo Ianez, Diego Torricelli, Jesus Tornero, Jose M. Azorin (2023). Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton. 10.82901/nemar.nm000320

Modality: eeg Subjects: 10 Recordings: 192 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton.

Data & curation Mario Ortiz · Luis de la Ossa · Eduardo Ianez · Diego Torricelli · Jesus Tornero · Jose M. Azorin
Senior author Jose M. Azorin
Year 2023 · Distributed via NeMAR

EEG · 27 ch200 HzBIDS 1.9.0Task · imagery2 sessions

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 NM000320

dataset = NM000320(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 = NM000320(cache_dir="./data", subject="01")

Advanced query

dataset = NM000320(
    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{nm000320,
  title = {Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton},
  author = {Mario Ortiz and Luis de la Ossa and Eduardo Ianez and Diego Torricelli and Jesus Tornero and Jose M. Azorin},
  doi = {10.82901/nemar.nm000320},
  url = {https://doi.org/10.82901/nemar.nm000320},
}

§ 02Study · The README

About This Dataset#

Motor imagery during walking with a lower-limb exoskeleton [1]_.

Code: Ortiz2023

Paradigm: imagery DOI: 10.1038/s41597-023-02243-7 Subjects: 10 Sessions per subject: 1 Events: relax=1, motor_imagery=2, regressive_count=3 Trial interval: [0, 9] s Runs per session: 16 Session IDs: 0, 1 File format: MAT

DOI

Ortiz2023

Acquisition

Sampling rate: 200.0 Hz Number of channels: 31 Channel types: eeg=27, eog=4 Channel names: F3, Fz, FC1, FCz, C1, Cz, CP1, CPz, FC5, FC3, C5, C3, CP5, CP3, P3, Pz, F4, FC2, FC4, FC6, C2, C4, CP2, CP4, C6, CP6, P4

View full README

DOI

Ortiz2023

Acquisition

Sampling rate: 200.0 Hz Number of channels: 31 Channel types: eeg=27, eog=4 Channel names: F3, Fz, FC1, FCz, C1, Cz, CP1, CPz, FC5, FC3, C5, C3, CP5, CP3, P3, Pz, F4, FC2, FC4, FC6, C2, C4, CP2, CP4, C6, CP6, P4 Montage: standard_1005 Hardware: Brain Products actiCHamp Reference: left ear lobe (A1) Ground: right ear lobe (A2) Sensor type: Ag/AgCl wet Line frequency: 50.0 Hz Online filters: 0.1 Hz high-pass and 50 Hz notch (online, hardware) Auxiliary channels: EOG (4 ch, horizontal, horizontal, vertical, vertical)

Participants

Number of subjects: 10 Health status: healthy Age: mean=28.7, std=4.8

Experimental Protocol

Paradigm: imagery Number of classes: 3 Class labels: relax, motor_imagery, regressive_count Trial duration: 9.0 s Study design: Kinesthetic motor imagery of gait alternated with relaxation and a regressive-count distractor task while walking with a fully assisted lower-limb exoskeleton on flat ground. Stimulus type: auditory Stimulus modalities: audio Synchronicity: cue-based Mode: offline

HED Event Annotations

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

     ├─ Sensory-event
     └─ Label/relax

motor_imagery

     ├─ Sensory-event
     └─ Label/motor_imagery

regressive_count

├─ Sensory-event
└─ Label/regressive_count

Tags

Pathology: healthy Modality: motor Type: imagery

Documentation

Description: EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton (DECODED, a EUROBENCH sub-project); flat-ground (EXPERIENCE) scenario. DOI: 10.1038/s41597-023-02243-7 License: CC-BY-4.0 Investigators: Mario Ortiz, Luis de la Ossa, Eduardo Ianez, Diego Torricelli, Jesus Tornero, Jose M. Azorin Institution: Miguel Hernandez University of Elche Country: ES Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.21185362.v2 Publication year: 2023

References

Ortiz, M., de la Ossa, L., Ianez, E., Torricelli, D., Tornero, J., & Azorin, J. M. (2023). An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton. Scientific Data, 10, 343. https://doi.org/10.1038/s41597-023-02243-7 Notes .. versionadded:: 1.8 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 Ethics and Integrity in Research Committee of Miguel Hernández University of Elche, Spain (reference DIS.JAP.05.20), and by the Ethics Committee of CSIC, Madrid, Spain (internal reference 091/2021). All participants signed an informed consent in accordance with the Declaration of Helsinki (Ortiz et al. 2023, Sci. Data 10:343, DOI 10.1038/s41597-023-02243-7).

Verbatim from the source:

All procedures were approved by the Ethics and Integrity in Research Committee of Miguel Hernández University of Elche (Spain) (Reference DIS.JAP.05.20) and the Ethics Committee of CSIC (Madrid, Spain) (Internal reference 091/2021). Consent for video and image recording was also given.

Source: cached paper .paper-audit/Ortiz2023/paper-10_1038_s41597_023_02243_7.txt (Ortiz et al. 2023, Sci. Data 10:343, DOI 10.1038/s41597-023-02243-7).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000320-blue)](https://doi.org/10.82901/nemar.nm000320) Ortiz2023 ========= Motor imagery during walking with a lower-limb exoskeleton [1]_. Dataset Overview —————-

Code: Ortiz2023 Paradigm: imagery DOI: 10.1038/s41597-023-02243-7 Subjects: 10 Sessions per subject: 1 Events: relax=1, motor_imagery=2, regressive_count=3 Trial interval: [0, 9] s Runs per session: 16 Session IDs: 0, 1 File format: MAT

Acquisition#

Sampling rate: 200.0 Hz Number of channels: 31 Channel types: eeg=27, eog=4 Channel names: F3, Fz, FC1, FCz, C1, Cz, CP1, CPz, FC5, FC3, C5, C3, CP5, CP3, P3, Pz, F4, FC2, FC4, FC6, C2, C4, CP2, CP4, C6, CP6, P4 Montage: standard_1005 Hardware: Brain Products actiCHamp Reference: left ear lobe (A1) Ground: right ear lobe (A2) Sensor type: Ag/AgCl wet Line frequency: 50.0 Hz Online filters: 0.1 Hz high-pass and 50 Hz notch (online, hardware) Auxiliary channels: EOG (4 ch, horizontal, horizontal, vertical, vertical)

Participants#

Number of subjects: 10 Health status: healthy Age: mean=28.7, std=4.8

Experimental Protocol#

Paradigm: imagery Number of classes: 3 Class labels: relax, motor_imagery, regressive_count Trial duration: 9.0 s Study design: Kinesthetic motor imagery of gait alternated with relaxation and a regressive-count distractor task while walking with a fully assisted lower-limb exoskeleton on flat ground. Stimulus type: auditory Stimulus modalities: audio Synchronicity: cue-based Mode: offline

HED Event Annotations#

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

├─ Sensory-event └─ Label/relax

motor_imagery
├─ Sensory-event └─ Label/motor_imagery

regressive_count
├─ Sensory-event └─ Label/regressive_count

Tags#

Pathology: healthy Modality: motor Type: imagery

Documentation#

Description: EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton (DECODED, a EUROBENCH sub-project); flat-ground (EXPERIENCE) scenario. DOI: 10.1038/s41597-023-02243-7 License: CC-BY-4.0 Investigators: Mario Ortiz, Luis de la Ossa, Eduardo Ianez, Diego Torricelli, Jesus Tornero, Jose M. Azorin Institution: Miguel Hernandez University of Elche Country: ES Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.21185362.v2 Publication year: 2023

References#

Ortiz, M., de la Ossa, L., Ianez, E., Torricelli, D., Tornero, J., & Azorin, J. M. (2023). An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton. Scientific Data, 10, 343. https://doi.org/10.1038/s41597-023-02243-7 Notes .. versionadded:: 1.8 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 Ethics and Integrity in Research Committee of Miguel Hernández University of Elche, Spain (reference DIS.JAP.05.20), and by the Ethics Committee of CSIC, Madrid, Spain (internal reference 091/2021). All participants signed an informed consent in accordance with the Declaration of Helsinki (Ortiz et al. 2023, Sci. Data 10:343, DOI 10.1038/s41597-023-02243-7). Verbatim from the source: > All procedures were approved by the Ethics and Integrity in Research Committee of Miguel Hernández University of Elche (Spain) (Reference DIS.JAP.05.20) and the Ethics Committee of CSIC (Madrid, Spain) (Internal reference 091/2021). Consent for video and image recording was also given. Source: cached paper .paper-audit/Ortiz2023/paper-10_1038_s41597_023_02243_7.txt (Ortiz et al. 2023, Sci. Data 10:343, DOI 10.1038/s41597-023-02243-7).

License: CC-BY-4.0

Authors:

Mario Ortiz
Luis de la Ossa
Eduardo Ianez
Diego Torricelli
Jesus Tornero
… and 1 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000320

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 29–29 yr, mean 28.0 yr)

25

Other · 10

Channel counts: 27 ch (n=192 recordings)

Sampling frequencies: 200.0 Hz (n=192 recordings)

Total recording duration: 3 h 59 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 27 ch · EEG · 200 Hz · 10 subjects, 192 recordings

Live trace viewer — sub-7 · ses-0 · task-imagery · run-11

Showing one representative recording out of 10 subjects and 192 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 · 27 sensors — 27 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.NM000320(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000320

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

class eegdash.dataset.NM000320(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton

Study:: nm000320 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000320.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 192; 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/nm000320 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000320 DOI: https://doi.org/10.82901/nemar.nm000320

Examples

>>> from eegdash.dataset import NM000320
>>> dataset = NM000320(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 — NM000320.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 nm000320 to reproduce the tutorial on this dataset.

Citation

Mario Ortiz, Luis de la Ossa, Eduardo Ianez, Diego Torricelli, Jesus Tornero, … (2023). Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton. 10.82901/nemar.nm000320

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000320.

Related & sibling datasets

NM000315EEG · 10 subj NM000294EEG · 12 subj NM000302EEG · 6 subj NM000300EEG · 10 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.nm000320

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000320`
Title	Ortiz2023: Motor imagery during walking with a lower-limb exoskeleton
Author (year)	—
Canonical	—
Importable as	`NM000320`
Year	2023
Authors	Mario Ortiz, Luis de la Ossa, Eduardo Ianez, Diego Torricelli, Jesus Tornero, Jose M. Azorin
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000320
Source links	OpenNeuro \| NeMAR \| Source URL

NM000320: 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#