EEGdash›NeMAR›NM000215

Iss. 215 · 38 subjects · 38 recordings · CC-BY-4.0

Dataset Brief · P300 dataset BI2014b from a "Brain Invaders" experiment

NM000215: eeg dataset, 38 subjects#

P300 dataset BI2014b from a “Brain Invaders” experiment

Citation: Louis Korczowski, Ekaterina Ostaschenko, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, Marco Congedo (2019). P300 dataset BI2014b from a “Brain Invaders” experiment.

38-participant EEG dataset — P300 dataset BI2014b from a "Brain Invaders" experiment.

Data & curation Louis Korczowski · Ekaterina Ostaschenko · Anton Andreev · Grégoire Cattan · Pedro Luiz Coelho Rodrigues · Violette Gautheret · …
Year 2019 · Distributed via NeMAR

EEG · 32 ch512 HzBIDS 1.9.0Task · p300HealthyVisualAttention

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 NM000215

dataset = NM000215(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 = NM000215(cache_dir="./data", subject="01")

Advanced query

dataset = NM000215(
    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{nm000215,
  title = {P300 dataset BI2014b from a "Brain Invaders" experiment},
  author = {Louis Korczowski and Ekaterina Ostaschenko and Anton Andreev and Grégoire Cattan and Pedro Luiz Coelho Rodrigues and Violette Gautheret and Marco Congedo},
}

§ 02Study · The README

About This Dataset#

P300 dataset BI2014b from a “Brain Invaders” experiment.

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

P300 dataset BI2014b from a “Brain Invaders” experiment

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

P300 dataset BI2014b from a “Brain Invaders” experiment

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

Data Structure

Trials: variable per session (9 levels, up to 8 attempts per level)
Blocks per session: 9
Block duration: variable, average ~33 seconds per level (5 minutes total for 9 levels) s
Trials context: 9 levels per game session, each with unique predefined spatial configuration of 36 aliens. Up to 8 attempts to destroy target per level.

Preprocessing

Data state: raw EEG with no digital filter applied, synchronized experimental tags using USB analog-to-digital converter to reduce jitter
Preprocessing applied: False
Notes: Experimental tags produced by Brain Invaders 2 were synchronized with EEG signals using USB analog-to-digital converter connected to g.USBamp trigger channel. This tagging procedure allows consistent tagging latency and jitter.

Signal Processing

Classifiers: RMDM (Riemannian Minimum Distance to Mean), Riemannian
Feature extraction: Covariance/Riemannian

Cross-Validation

Evaluation type: cross_session

Performance (Original Study)

Classifier: real-time adaptive RMDM classifier (calibration-free procedure)

BCI Application

Applications: gaming
Environment: small room with 24’ screen, subjects sitting side by side at ~125cm distance, experimenter in adjacent room with one-way glass window
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual
Type: Perception

Documentation

Description: EEG recordings of 38 subjects playing in pairs to the multi-user version of Brain Invaders P300-based BCI. Contains three conditions: Solo1, Solo2, and Collaboration.
DOI: 10.5281/zenodo.3267301
Associated paper DOI: hal-02173958
License: CC-BY-4.0
Investigators: Louis Korczowski, Ekaterina Ostaschenko, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, Marco Congedo
Senior author: Marco Congedo
Institution: 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.3267301
Publication year: 2019
Ethics approval: Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle)
Acknowledgements: At the end of the experiment two tickets of cinema were offered to each subject, for a total value of 15 euros per subject.
Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface (BCI), Experiment, Collaboration, Multi-User, Hyperscanning

Abstract

We describe the experimental procedures for a dataset containing electroencephalographic (EEG) recordings of 38 subjects playing in pairs to the multi-user version of a visual P300-based Brain-Computer Interface (BCI) named Brain Invaders. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response. EEG data were recorded using 32 active wet electrodes per subject (total: 64 electrodes) during three randomised conditions (Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014.

Methodology

Multi-user hyperscanning P300 BCI experiment designed to study inter-brain synchrony. Participants played Brain Invaders 2 in three conditions: Solo1 (player1 plays, player2 watches cross), Solo2 (roles reversed), and Collaboration (4 game sessions with both players). Each game session consisted of 9 levels with predefined alien configurations. A repetition used 12 flashes of pseudo-random groups of 6 symbols, ensuring each symbol flashed twice per repetition (1:5 Target:Non-Target ratio). Real-time adaptive RMDM classifier provided online feedback. Control condition (non-playing participant) allowed correction for fake inter-brain synchrony.

References

Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders Solo versus Collaboration: Multi-User P300-Based Brain-Computer Interface Dataset (BI2014b). https://hal.archives-ouvertes.fr/hal-02173958 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=38, range 24–24 yr, mean 24.0 yr)

20

Other · 38

Channel counts: 32 ch (n=38 recordings)

Sampling frequencies: 512.0 Hz (n=38 recordings)

Total recording duration: 2 h 21 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 512 Hz · 38 subjects, 38 recordings

Live trace viewer — sub-13 · ses-0 · task-p300 · run-0

Showing one representative recording out of 38 subjects and 38 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.NM000215(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Korczowski2014_P300

Canonical—

Importable asNM000215 · Korczowski2014_P300

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

class eegdash.dataset.NM000215(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P300 dataset BI2014b from a “Brain Invaders” experiment

Study:: nm000215 (NeMAR)
Author (year):: Korczowski2014_P300
Canonical:: —

Also importable as: NM000215, Korczowski2014_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 38; recordings: 38; 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/nm000215 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000215

Examples

>>> from eegdash.dataset import NM000215
>>> dataset = NM000215(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 — NM000215.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 nm000215 to reproduce the tutorial on this dataset.

Citation

Louis Korczowski, Ekaterina Ostaschenko, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, … (2019). P300 dataset BI2014b from a "Brain Invaders" experiment.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000217EEG · 44 subjSame authors NM000201EEG · 24 subj NM000206EEG · 15 subj NM000343EEG · 15 subj NM000346EEG · 12 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	`NM000215`
Title	P300 dataset BI2014b from a “Brain Invaders” experiment
Author (year)	`Korczowski2014_P300`
Canonical	—
Importable as	`NM000215`, `Korczowski2014_P300`
Year	2019
Authors	Louis Korczowski, Ekaterina Ostaschenko, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, Marco Congedo
License	CC-BY-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000215: eeg dataset, 38 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#