EEGdash›NeMAR›NM000318

Iss. 318 · 20 subjects · 99 recordings · CC-BY-4.0

Dataset Brief · Batista2022

NM000318: eeg dataset, 20 subjects#

Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training

Access recordings and metadata through EEGDash.

Citation: Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos (2022). Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training. 10.82901/nemar.nm000318

Modality: eeg Subjects: 20 Recordings: 99 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

20-participant EEG dataset — Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training.

Data & curation Diogo Batista · Gustavo Caetano · Mathis Fleury · Patricia Figueiredo · Athanasios Vourvopoulos
Senior author Athanasios Vourvopoulos
Year 2022 · Distributed via NeMAR

EEG · 32 ch500 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 NM000318

dataset = NM000318(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 = NM000318(cache_dir="./data", subject="01")

Advanced query

dataset = NM000318(
    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{nm000318,
  title = {Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training},
  author = {Diogo Batista and Gustavo Caetano and Mathis Fleury and Patricia Figueiredo and Athanasios Vourvopoulos},
  doi = {10.82901/nemar.nm000318},
  url = {https://doi.org/10.82901/nemar.nm000318},
}

§ 02Study · The README

About This Dataset#

Motor-imagery EEG during NeuRow VR/haptics BCI training [1]_.

Code: Batista2022

Paradigm: imagery DOI: 10.5281/zenodo.7664068 Subjects: 20 Sessions per subject: 1 Events: left_hand=7, right_hand=8 Trial interval: [0, 5] s Runs per session: 5 File format: BrainVision

DOI

Batista2022

Acquisition

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, ecg=1, resp=1, 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

Batista2022

Acquisition

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, ecg=1, resp=1, 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: 20 Health status: healthy Age: mean=24.79, std=3.54

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 5.0 s Study design: Within-subject, randomized-order cue-based left- vs right-hand motor imagery of a bimanual rowing task, compared across Graz and NeuRow virtual-reality conditions with optional haptic and head-mounted-display feedback, plus a motor-execution control. Feedback type: visual Stimulus type: visual Stimulus modalities: visual, tactile Synchronicity: cue-based Mode: online Training/test split: False Instructions: Imagine moving the left or right paddle (bimanual rowing) following the directional cue.

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: 5.0 s

Data Structure

Blocks per session: 5 Trials context: One lab session per subject with up to five imagery runs (one per condition: MI, MIMO, MIMOHP, MIMOVR, MIMOVRHP; the motor-execution ME control is excluded). Trials are cue-locked to the left/right-hand marker with a 5 s imagery/feedback window.

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 PPG, respiration, ECG and accelerometry from 20 healthy volunteers performing cued left/right-hand motor imagery of a rowing task during VR/haptics BCI training. DOI: 10.5281/zenodo.7664068 License: CC-BY-4.0 Investigators: Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos Institution: Instituto Superior Tecnico, Universidade de Lisboa Country: PT Repository: Zenodo Data URL: https://zenodo.org/records/7664069 Publication year: 2022 Keywords: motor imagery, BCI, brain-computer interface, EEG, virtual reality, haptics, neurorehabilitation

References

Batista, D., Caetano, G., Fleury, M., Figueiredo, P., and Vourvopoulos, A. (2022). Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics. Zenodo. DOI: https://doi.org/10.5281/zenodo.7664068 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

Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Batista et al. 2022, Zenodo DOI 10.5281/zenodo.7664068). The source does not name the approving ethics committee in-line.

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 7664068 / 7664069 description (https://zenodo.org/records/7664068).

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.nm000318-blue)](https://doi.org/10.82901/nemar.nm000318) Batista2022 =========== Motor-imagery EEG during NeuRow VR/haptics BCI training [1]_. Dataset Overview —————-

Code: Batista2022 Paradigm: imagery DOI: 10.5281/zenodo.7664068 Subjects: 20 Sessions per subject: 1 Events: left_hand=7, right_hand=8 Trial interval: [0, 5] s Runs per session: 5 File format: BrainVision

Acquisition#

Sampling rate: 500.0 Hz Number of channels: 38 Channel types: eeg=32, ecg=1, resp=1, 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: 20 Health status: healthy Age: mean=24.79, std=3.54

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 5.0 s Study design: Within-subject, randomized-order cue-based left- vs right-hand motor imagery of a bimanual rowing task, compared across Graz and NeuRow virtual-reality conditions with optional haptic and head-mounted-display feedback, plus a motor-execution control. Feedback type: visual Stimulus type: visual Stimulus modalities: visual, tactile Synchronicity: cue-based Mode: online Training/test split: False Instructions: Imagine moving the left or right paddle (bimanual rowing) following the directional cue.

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: 5.0 s

Data Structure#

Blocks per session: 5 Trials context: One lab session per subject with up to five imagery runs (one per condition: MI, MIMO, MIMOHP, MIMOVR, MIMOVRHP; the motor-execution ME control is excluded). Trials are cue-locked to the left/right-hand marker with a 5 s imagery/feedback window.

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 PPG, respiration, ECG and accelerometry from 20 healthy volunteers performing cued left/right-hand motor imagery of a rowing task during VR/haptics BCI training. DOI: 10.5281/zenodo.7664068 License: CC-BY-4.0 Investigators: Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos Institution: Instituto Superior Tecnico, Universidade de Lisboa Country: PT Repository: Zenodo Data URL: https://zenodo.org/records/7664069 Publication year: 2022 Keywords: motor imagery, BCI, brain-computer interface, EEG, virtual reality, haptics, neurorehabilitation

References#

Batista, D., Caetano, G., Fleury, M., Figueiredo, P., and Vourvopoulos, A. (2022). Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics. Zenodo. DOI: https://doi.org/10.5281/zenodo.7664068 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 Written informed consent was obtained from all participants in accordance with the 1964 Declaration of Helsinki (Batista et al. 2022, Zenodo DOI 10.5281/zenodo.7664068). The source does not name the approving ethics committee in-line. 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 7664068 / 7664069 description (https://zenodo.org/records/7664068). 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:

Diogo Batista
Gustavo Caetano
Mathis Fleury
Patricia Figueiredo
Athanasios Vourvopoulos

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000318

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 25–25 yr, mean 24.0 yr)

20

Other · 20

Channel counts: 32 ch (n=99 recordings)

Sampling frequencies: 500.0 Hz (n=99 recordings)

Total recording duration: 14 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 500 Hz · 20 subjects, 99 recordings

Live trace viewer — sub-7 · ses-0lab1 · task-imagery · run-3

Showing one representative recording out of 20 subjects and 99 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.NM000318(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000318

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

class eegdash.dataset.NM000318(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training

Study:: nm000318 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000318.

Modality: eeg; Subject type: Unknown. Subjects: 20; recordings: 99; 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/nm000318 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000318 DOI: https://doi.org/10.82901/nemar.nm000318

Examples

>>> from eegdash.dataset import NM000318
>>> dataset = NM000318(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 — NM000318.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 nm000318 to reproduce the tutorial on this dataset.

Citation

Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos (2022). Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training. 10.82901/nemar.nm000318

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000318.

Related & sibling datasets

NM000312EEG · 27 subj NM000306EEG · 15 subj NM000293EEG · 11 subj NM000385EEG · 14 subj NM000319EEG · 8 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.nm000318

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000318`
Title	Batista2022: Motor-imagery EEG during NeuRow VR/haptics BCI training
Author (year)	—
Canonical	—
Importable as	`NM000318`
Year	2022
Authors	Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000318
Source links	OpenNeuro \| NeMAR \| Source URL

NM000318: eeg dataset, 20 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#