EEGdash›NeMAR›NM000292

Iss. 292 · 26 subjects · 26 recordings · CC-BY-4.0

Dataset Brief · Vagaja2023

NM000292: eeg dataset, 26 subjects#

Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR

Access recordings and metadata through EEGDash.

Citation: Katarina Vagaja, Athanasios Vourvopoulos (2023). Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR. 10.82901/nemar.nm000292

Modality: eeg Subjects: 26 Recordings: 26 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

26-participant EEG dataset — Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR.

Data & curation Katarina Vagaja · Athanasios Vourvopoulos
Senior author Athanasios Vourvopoulos
Year 2023 · Distributed via NeMAR

EEG · 32 ch500 HzBIDS 1.9.0Task · imageryHealthyVisualMotor

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 NM000292

dataset = NM000292(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 = NM000292(cache_dir="./data", subject="01")

Advanced query

dataset = NM000292(
    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{nm000292,
  title = {Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR},
  author = {Katarina Vagaja and Athanasios Vourvopoulos},
  doi = {10.82901/nemar.nm000292},
  url = {https://doi.org/10.82901/nemar.nm000292},
}

§ 02Study · The README

About This Dataset#

Motor-imagery EEG during embodiment-primed MI-BCI training in VR [1]_.

Code: Vagaja2023

Paradigm: imagery DOI: 10.5281/zenodo.8086086 Subjects: 26 Sessions per subject: 1 Events: left_hand=7, right_hand=8 Trial interval: [0, 10] s File format: BrainVision

DOI

Vagaja2023

Acquisition

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, emg=2, misc=4 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2

View full README

DOI

Vagaja2023

Acquisition

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, emg=2, misc=4 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2 Montage: standard_1020 Hardware: LiveAmp 32 (Brain Products GmbH) Software: BrainVision Recorder Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: actiCAP Electrode type: active

Participants

Number of subjects: 26 Health status: healthy Gender distribution: male=10, female=16 Handedness: {‘right’: 26} BCI experience: mixed (3 of 26 with prior BCI experience)

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 10.0 s Study design: Between-subject, randomized-group cue-based left- vs right-hand motor imagery in immersive VR, comparing an embodied (virtual-hand-illusion) priming group against a non-embodied control group, each with a single MI training run of 40 trials. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: False Instructions: Imagine moving the left or right hand following the directional cue while immersed in the virtual-reality environment.

HED Event Annotations

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

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right, Hand

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand Imagery duration: 10.0 s

Data Structure

Blocks per session: 1 Trials context: One lab session per subject. Each subject has resting-state, embodiment and MI BrainVision recordings, but only the MI run carries left/right-hand class markers and is exposed here. Trials are cue-locked to the left/right-hand marker with a 10 s imagery/feedback window (20 trials per class).

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Frequency bands: mu=[8.0, 12.0] Hz; beta=[12.0, 30.0] Hz

Cross-Validation

Evaluation type: within_subject

BCI Application

Applications: motor rehabilitation, BCI training Environment: lab Online feedback: True

Tags

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation

Description: Motor-imagery EEG plus bipolar EMG, skin temperature and accelerometry from 26 healthy volunteers performing cued left/right-hand motor imagery during embodiment-primed MI-BCI training in virtual reality. DOI: 10.5281/zenodo.8086086 License: CC-BY-4.0 Investigators: Katarina Vagaja, Athanasios Vourvopoulos Institution: Instituto Superior Tecnico, Universidade de Lisboa Country: PT Repository: Zenodo Data URL: https://zenodo.org/records/8086086 Publication year: 2023 Keywords: motor imagery, BCI, brain-computer interface, EEG, virtual reality, embodiment, virtual hand illusion, neurorehabilitation

References

Vagaja, K., and Vourvopoulos, A. (2023). Electrophysiological Signals of Embodiment and MI-BCI Training in VR. Zenodo. DOI: https://doi.org/10.5281/zenodo.8086086 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

Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Vagaja & Vourvopoulos 2023, Zenodo DOI 10.5281/zenodo.8086086). The source does not name the approving ethics committee.

Verbatim from the source:

All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki.

Source: Zenodo record 8086086 description (https://zenodo.org/records/8086086).

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.nm000292-blue)](https://doi.org/10.82901/nemar.nm000292) Vagaja2023 ========== Motor-imagery EEG during embodiment-primed MI-BCI training in VR [1]_. Dataset Overview —————-

Code: Vagaja2023 Paradigm: imagery DOI: 10.5281/zenodo.8086086 Subjects: 26 Sessions per subject: 1 Events: left_hand=7, right_hand=8 Trial interval: [0, 10] s File format: BrainVision

Acquisition#

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, emg=2, misc=4 Channel names: Fp1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, TP9, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2 Montage: standard_1020 Hardware: LiveAmp 32 (Brain Products GmbH) Software: BrainVision Recorder Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Cap manufacturer: Brain Products GmbH Cap model: actiCAP Electrode type: active

Participants#

Number of subjects: 26 Health status: healthy Gender distribution: male=10, female=16 Handedness: {‘right’: 26} BCI experience: mixed (3 of 26 with prior BCI experience)

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 10.0 s Study design: Between-subject, randomized-group cue-based left- vs right-hand motor imagery in immersive VR, comparing an embodied (virtual-hand-illusion) priming group against a non-embodied control group, each with a single MI training run of 40 trials. Feedback type: visual Stimulus type: visual Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: False Instructions: Imagine moving the left or right hand following the directional cue while immersed in the virtual-reality environment.

HED Event Annotations#

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

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Left, Hand

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand Imagery duration: 10.0 s

Data Structure#

Blocks per session: 1 Trials context: One lab session per subject. Each subject has resting-state, embodiment and MI BrainVision recordings, but only the MI run carries left/right-hand class markers and is exposed here. Trials are cue-locked to the left/right-hand marker with a 10 s imagery/feedback window (20 trials per class).

Preprocessing#

Data state: raw Preprocessing applied: False

Signal Processing#

Frequency bands: mu=[8.0, 12.0] Hz; beta=[12.0, 30.0] Hz

Cross-Validation#

Evaluation type: within_subject

BCI Application#

Applications: motor rehabilitation, BCI training Environment: lab Online feedback: True

Tags#

Pathology: healthy Modality: motor Type: Motor Imagery

Documentation#

Description: Motor-imagery EEG plus bipolar EMG, skin temperature and accelerometry from 26 healthy volunteers performing cued left/right-hand motor imagery during embodiment-primed MI-BCI training in virtual reality. DOI: 10.5281/zenodo.8086086 License: CC-BY-4.0 Investigators: Katarina Vagaja, Athanasios Vourvopoulos Institution: Instituto Superior Tecnico, Universidade de Lisboa Country: PT Repository: Zenodo Data URL: https://zenodo.org/records/8086086 Publication year: 2023 Keywords: motor imagery, BCI, brain-computer interface, EEG, virtual reality, embodiment, virtual hand illusion, neurorehabilitation

References#

Vagaja, K., and Vourvopoulos, A. (2023). Electrophysiological Signals of Embodiment and MI-BCI Training in VR. Zenodo. DOI: https://doi.org/10.5281/zenodo.8086086 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 Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Vagaja & Vourvopoulos 2023, Zenodo DOI 10.5281/zenodo.8086086). The source does not name the approving ethics committee. Verbatim from the source: > All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki. Source: Zenodo record 8086086 description (https://zenodo.org/records/8086086). 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:

Katarina Vagaja
Athanasios Vourvopoulos

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000292

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=26 recordings)

Sampling frequencies: 500.0 Hz (n=26 recordings)

Total recording duration: 6 h 48 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 500 Hz · 26 subjects, 26 recordings

Live trace viewer — sub-10 · ses-0 · task-imagery · run-0

Showing one representative recording out of 26 subjects and 26 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.NM000292(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000292

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

class eegdash.dataset.NM000292(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR

Study:: nm000292 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000292.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 26; recordings: 26; 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/nm000292 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000292 DOI: https://doi.org/10.82901/nemar.nm000292

Examples

>>> from eegdash.dataset import NM000292
>>> dataset = NM000292(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 — NM000292.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 nm000292 to reproduce the tutorial on this dataset.

Citation

Katarina Vagaja, Athanasios Vourvopoulos (2023). Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR. 10.82901/nemar.nm000292

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000292.

Related & sibling datasets

ON005342EEG · 32 subj NM000237EEG · 20 subj NM000177EEG · 18 subj NM000161EEG · 20 subj ON008446EEG · 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.nm000292

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000292`
Title	Vagaja2023: Motor-imagery EEG during embodiment-primed MI-BCI training in VR
Author (year)	—
Canonical	—
Importable as	`NM000292`
Year	2023
Authors	Katarina Vagaja, Athanasios Vourvopoulos
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000292
Source links	OpenNeuro \| NeMAR \| Source URL

NM000292: eeg dataset, 26 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Signal Processing#

Cross-Validation#

BCI Application#

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#