EEGdash›NeMAR›NM000236

Iss. 236 · 21 subjects · 2520 recordings · CC-BY-4.0

Dataset Brief · Dataset of an EEG-based BCI experiment in Virtual Reality usi…

NM000236: eeg dataset, 21 subjects#

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Citation: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

21-participant EEG dataset — Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

Data & curation Grégoire Cattan · Anton Andreev · Pedro Luiz Coelho Rodrigues · Marco Congedo
Year 2019 · Distributed via NeMAR
Funding IHMTEK Company (Interaction Homme-Machine Technologie)

EEG · 16 ch512 HzBIDS 1.9.0Task · p3002 sessionsHealthyVisualAttention

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 NM000236

dataset = NM000236(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 = NM000236(cache_dir="./data", subject="01")

Advanced query

dataset = NM000236(
    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{nm000236,
  title = {Dataset of an EEG-based BCI experiment in Virtual Reality using P300},
  author = {Grégoire Cattan and Anton Andreev and Pedro Luiz Coelho Rodrigues and Marco Congedo},
}

§ 02Study · The README

About This Dataset#

Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

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

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

Detected paradigm: p300
Number of targets: 1
Number of repetitions: 12

Data Structure

Trials: {‘target’: 120, ‘non_target’: 600}
Trials per class: target=120, non_target=600
Blocks per session: 12
Trials context: per session: 12 blocks × 5 repetitions × 12 flashes per repetition (2 target, 10 non-target)

Preprocessing

Data state: raw EEG with software tagging via USB (note: tagging introduces jitter and latency - mean 38ms in PC, 117ms in VR)
Preprocessing applied: False
Notes: mean tagging latency: ~38 ms in PC, ~117 ms in VR due to different hardware/software setup; these latencies should be used to correct ERPs

Signal Processing

Classifiers: xDAWN, Riemannian
Feature extraction: Covariance/Riemannian, xDAWN

Cross-Validation

Evaluation type: cross_session

BCI Application

Applications: speller
Environment: PC and Virtual Reality (VRElegiant HMD with Huawei Ascend Mate 7 smartphone)
Online feedback: False

Tags

Pathology: Healthy
Modality: Visual
Type: Perception

Documentation

Description: EEG recordings of 21 subjects doing a visual P300 experiment on PC and VR to compare BCI performance and user experience
DOI: 10.5281/zenodo.2605204
Associated paper DOI: hal-02078533v3
License: CC-BY-4.0
Investigators: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo
Senior author: Marco Congedo
Institution: GIPSA-lab
Department: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP
Address: GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France
Country: FR
Repository: Zenodo
Data URL: https://doi.org/10.5281/zenodo.2605204
Publication year: 2019
Funding: IHMTEK Company (Interaction Homme-Machine Technologie)
Ethics approval: Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle)
Acknowledgements: promoted by the IHMTEK Company
Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface (BCI), Virtual Reality (VR), experiment

Abstract

Dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC and VR. The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240–600 ms after stimulus onset. The experiment compares P300-based BCI on PC vs VR headset (passive HMD with smartphone) concerning physiological, subjective and performance aspects. EEG recorded with 16 electrodes. Experiment conducted at GIPSA-lab in 2018.

Methodology

Two randomized sessions (PC and VR). Each session: 12 blocks of 5 repetitions. Each repetition: 12 flashes of groups of 6 symbols, ensuring each symbol flashes exactly 2 times. Target flashes twice per repetition (2 target flashes), non-target flashes 10 times. Random feedback given after each repetition (70% expected accuracy). P300 interface: 6x6 matrix of white flashing crosses with red-squared target. VR used passive HMD (VRElegiant) with Huawei Mate 7 smartphone. IMU deactivated to prevent drift. Unity engine used for identical visual stimulation across PC and VR.

References

G. Cattan, A. Andreev, P. L. C. Rodrigues, and M. Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer. Research Report, GIPSA-lab; IHMTEK. https://doi.org/10.5281/zenodo.2605204 .. versionadded:: 0.5.0 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=21, range 26–26 yr, mean 26.0 yr)

25

Other · 21

Channel counts: 16 ch (n=2520 recordings)

Sampling frequencies: 512.0 Hz (n=2520 recordings)

Total recording duration: 4 h 5 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 512 Hz · 21 subjects, 2520 recordings

Live trace viewer — sub-9 · ses-1PC · task-p300 · run-59

Showing one representative recording out of 21 subjects and 2520 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 · 16 sensors — 16 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.NM000236(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Cattan2019_P300

Canonical—

Importable asNM000236 · Cattan2019_P300

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

class eegdash.dataset.NM000236(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Study:: nm000236 (NeMAR)
Author (year):: Cattan2019_P300
Canonical:: —

Also importable as: NM000236, Cattan2019_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 2520; 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/nm000236 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000236

Examples

>>> from eegdash.dataset import NM000236
>>> dataset = NM000236(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 — NM000236.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 nm000236 to reproduce the tutorial on this dataset.

Citation

Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000213EEG · 30 subj NM000223EEG · 15 subj NM000340EEG · 20 subj NM000208EEG · 14 subj NM000204EEG · 14 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000236`
Title	Dataset of an EEG-based BCI experiment in Virtual Reality using P300
Author (year)	`Cattan2019_P300`
Canonical	—
Importable as	`NM000236`, `Cattan2019_P300`
Year	2019
Authors	Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo
License	CC-BY-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000236: eeg dataset, 21 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#