EEGdash›NeMAR›NM000293

Iss. 293 · 11 subjects · 11 recordings · CC-BY-4.0

Dataset Brief · MOVING2024

NM000293: eeg dataset, 11 subjects#

MOVING2024: Motor imagery / motor execution dataset from the MOVING study

Access recordings and metadata through EEGDash.

Citation: Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, Giuseppe Placidi (2024). MOVING2024: Motor imagery / motor execution dataset from the MOVING study. 10.82901/nemar.nm000293

Modality: eeg Subjects: 11 Recordings: 11 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

11-participant EEG dataset — MOVING2024: Motor imagery / motor execution dataset from the MOVING study.

Data & curation Enrico Mattei · Daniele Lozzi · Alessandro Di Matteo · Alessia Cipriani · Costanzo Manes · Giuseppe Placidi
Senior author Giuseppe Placidi
Year 2024 · Distributed via NeMAR

EEG · 32 ch500 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 NM000293

dataset = NM000293(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 = NM000293(cache_dir="./data", subject="01")

Advanced query

dataset = NM000293(
    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{nm000293,
  title = {MOVING2024: Motor imagery / motor execution dataset from the MOVING study},
  author = {Enrico Mattei and Daniele Lozzi and Alessandro Di Matteo and Alessia Cipriani and Costanzo Manes and Giuseppe Placidi},
  doi = {10.82901/nemar.nm000293},
  url = {https://doi.org/10.82901/nemar.nm000293},
}

§ 02Study · The README

About This Dataset#

Motor imagery / motor execution dataset from the MOVING study [1]_ [2]_.

Code: MOVING2024

Paradigm: imagery DOI: 10.3390/s24165207 Subjects: 11 Sessions per subject: 1 Events: rest=1, open_close=2, wrist_rotation=3, finger_tapping=4 Trial interval: [0, 6] s File format: EDF

DOI

MOVING2024

Acquisition

Sampling rate: 500.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: P7, P4, Cz, Pz, P3, P8, O1, O2, T8, F8, C4, F4, Fp2, Fz, C3, F3, Fp1, T7, F7, Oz, PO4, FC6, FC2, AF4, CP6, CP2, CP1, CP5, FC1, FC5, AF3, PO3

View full README

DOI

MOVING2024

Acquisition

Sampling rate: 500.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: P7, P4, Cz, Pz, P3, P8, O1, O2, T8, F8, C4, F4, Fp2, Fz, C3, F3, Fp1, T7, F7, Oz, PO4, FC6, FC2, AF4, CP6, CP2, CP1, CP5, FC1, FC5, AF3, PO3 Montage: 10-20 Hardware: Neuroelectrics Enobio 32 (dry electrodes, wireless) Reference: CMS/DRL Sensor type: dry Line frequency: 50.0 Hz Electrode type: dry

Participants

Number of subjects: 11 Health status: healthy

Experimental Protocol

Paradigm: imagery Number of classes: 4 Class labels: rest, open_close, wrist_rotation, finger_tapping Trial duration: 6.0 s Study design: rest -> motor imagery -> motor execution triplet for three right-hand movements (open/close, wrist rotation, finger tapping), 8 repetitions per subject. 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

open_close

     ├─ Sensory-event
     └─ Label/open_close

wrist_rotation

     ├─ Sensory-event
     └─ Label/wrist_rotation

finger_tapping

├─ Sensory-event
└─ Label/finger_tapping

Tags

Modality: Motor Type: Motor Imagery, Motor Execution

Documentation

Description: Multi-modal dataset pairing 32-channel dry EEG with Virtual Glove hand-kinematic tracking during motor imagery and motor execution of three right-hand movements. DOI: 10.3390/s24165207 License: CC-BY-4.0 Investigators: Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, Giuseppe Placidi Institution: University of L’Aquila Country: IT Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.12804784 Publication year: 2024

References

Mattei, E., Lozzi, D., Di Matteo, A., Cipriani, A., Manes, C., & Placidi, G. (2024). MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors, 24(16), 5207. DOI: https://doi.org/10.3390/s24165207 Mattei, E., Lozzi, D., Di Matteo, A., Placidi, G., Manes, C., & Cipriani, A. (2024). MOVING dataset [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.12804784 Notes The fixed movement order can confound class with position within a block; random trial-level splits do not remove this protocol limitation. Rest has 24 trials versus 8 for each movement (48 trials per subject).

Extraction of edf.rar requires unrar, unar or 7z to be installed on the system. The Sensors paper describes the protocol (32 dry Enobio electrodes, 2 s fixation + 6 s action, eight repetitions of the triplet, ~10 min) but states neither the number of participants nor the raw sampling rate; the eleven subjects come from the Zenodo record and the 500 Hz rate from the EDF headers (paper audit, 2026-09-30). .. 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

The source publication’s Institutional Review Board Statement and Informed Consent Statement cite the University of L’Aquila participant information/consent form (https://www.univaq.it/include/utilities/blob.php?item=file&table=allegato&id=6252); see Mattei et al. 2024, Sensors 24(16):5207, DOI 10.3390/s24165207. The published article does not name the approving IRB in-line.

Verbatim from the source:

## Institutional Review Board Statementn https://www.univaq.it/include/utilities/blob.php?item=file&table=allegato&id=6252) accessed on 15 June 2024.

Source: cached paper .paper-audit/MOVING2024/paper-10_3390_s24165207.txt (Mattei et al. 2024, Sensors 24:5207, DOI 10.3390/s24165207).

Note: The source statement is incomplete (no committee named); defers to the primary publication for the full ethics record.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000293-blue)](https://doi.org/10.82901/nemar.nm000293) MOVING2024 ========== Motor imagery / motor execution dataset from the MOVING study [1]_ [2]_. Dataset Overview —————-

Code: MOVING2024 Paradigm: imagery DOI: 10.3390/s24165207 Subjects: 11 Sessions per subject: 1 Events: rest=1, open_close=2, wrist_rotation=3, finger_tapping=4 Trial interval: [0, 6] s File format: EDF

Acquisition#

Sampling rate: 500.0 Hz Number of channels: 35 Channel types: eeg=32, misc=3 Channel names: P7, P4, Cz, Pz, P3, P8, O1, O2, T8, F8, C4, F4, Fp2, Fz, C3, F3, Fp1, T7, F7, Oz, PO4, FC6, FC2, AF4, CP6, CP2, CP1, CP5, FC1, FC5, AF3, PO3 Montage: 10-20 Hardware: Neuroelectrics Enobio 32 (dry electrodes, wireless) Reference: CMS/DRL Sensor type: dry Line frequency: 50.0 Hz Electrode type: dry

Participants#

Number of subjects: 11 Health status: healthy

Experimental Protocol#

Paradigm: imagery Number of classes: 4 Class labels: rest, open_close, wrist_rotation, finger_tapping Trial duration: 6.0 s Study design: rest -> motor imagery -> motor execution triplet for three right-hand movements (open/close, wrist rotation, finger tapping), 8 repetitions per subject. 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

open_close
├─ Sensory-event └─ Label/open_close

wrist_rotation
├─ Sensory-event └─ Label/wrist_rotation

finger_tapping
├─ Sensory-event └─ Label/finger_tapping

Tags#

Modality: Motor Type: Motor Imagery, Motor Execution

Documentation#

Description: Multi-modal dataset pairing 32-channel dry EEG with Virtual Glove hand-kinematic tracking during motor imagery and motor execution of three right-hand movements. DOI: 10.3390/s24165207 License: CC-BY-4.0 Investigators: Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, Giuseppe Placidi Institution: University of L’Aquila Country: IT Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.12804784 Publication year: 2024

References#

Mattei, E., Lozzi, D., Di Matteo, A., Cipriani, A., Manes, C., & Placidi, G. (2024). MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors, 24(16), 5207. DOI: https://doi.org/10.3390/s24165207 Mattei, E., Lozzi, D., Di Matteo, A., Placidi, G., Manes, C., & Cipriani, A. (2024). MOVING dataset [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.12804784 Notes The fixed movement order can confound class with position within a block; random trial-level splits do not remove this protocol limitation. Rest has 24 trials versus 8 for each movement (48 trials per subject). Extraction of edf.rar requires unrar, unar or 7z to be installed on the system. The Sensors paper describes the protocol (32 dry Enobio electrodes, 2 s fixation + 6 s action, eight repetitions of the triplet, ~10 min) but states neither the number of participants nor the raw sampling rate; the eleven subjects come from the Zenodo record and the 500 Hz rate from the EDF headers (paper audit, 2026-09-30). .. 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 The source publication’s Institutional Review Board Statement and Informed Consent Statement cite the University of L’Aquila participant information/consent form (https://www.univaq.it/include/utilities/blob.php?item=file&table=allegato&id=6252); see Mattei et al. 2024, Sensors 24(16):5207, DOI 10.3390/s24165207. The published article does not name the approving IRB in-line. Verbatim from the source: > ## Institutional Review Board Statementn https://www.univaq.it/include/utilities/blob.php?item=file&table=allegato&id=6252) accessed on 15 June 2024. Source: cached paper .paper-audit/MOVING2024/paper-10_3390_s24165207.txt (Mattei et al. 2024, Sensors 24:5207, DOI 10.3390/s24165207). Note: The source statement is incomplete (no committee named); defers to the primary publication for the full ethics record.

License: CC-BY-4.0

Authors:

Enrico Mattei
Daniele Lozzi
Alessandro Di Matteo
Alessia Cipriani
Costanzo Manes
… and 1 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000293

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=11 recordings)

Sampling frequencies: 500.0 Hz (n=11 recordings)

Total recording duration: 1 h 46 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 500 Hz · 11 subjects, 11 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 11 subjects and 11 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 · 32 sensors — 32 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.NM000293(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000293

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

class eegdash.dataset.NM000293(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MOVING2024: Motor imagery / motor execution dataset from the MOVING study

Study:: nm000293 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000293.

Modality: eeg; Subject type: Unknown. Subjects: 11; recordings: 11; 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/nm000293 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000293 DOI: https://doi.org/10.82901/nemar.nm000293

Examples

>>> from eegdash.dataset import NM000293
>>> dataset = NM000293(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 — NM000293.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 nm000293 to reproduce the tutorial on this dataset.

Citation

Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, … (2024). MOVING2024: Motor imagery / motor execution dataset from the MOVING study. 10.82901/nemar.nm000293

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000293.

Related & sibling datasets

NM000319EEG · 8 subj NM000306EEG · 15 subj NM000385EEG · 14 subj NM000307EEG · 2 subj NM000318EEG · 20 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.nm000293

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000293`
Title	MOVING2024: Motor imagery / motor execution dataset from the MOVING study
Author (year)	—
Canonical	—
Importable as	`NM000293`
Year	2024
Authors	Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes, Giuseppe Placidi
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000293
Source links	OpenNeuro \| NeMAR \| Source URL

NM000293: eeg dataset, 11 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#