EEGdash›NeMAR›NM000244

Iss. 244 · 64 subjects · 64 recordings · CC-BY-4.0

Dataset Brief · P300 dataset BI2014a from a "Brain Invaders" experiment

NM000244: eeg dataset, 64 subjects#

P300 dataset BI2014a 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 BI2014a from a “Brain Invaders” experiment.

64-participant EEG dataset — P300 dataset BI2014a 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 · 16 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 NM000244

dataset = NM000244(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 = NM000244(cache_dir="./data", subject="01")

Advanced query

dataset = NM000244(
    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{nm000244,
  title = {P300 dataset BI2014a 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 BI2014a from a “Brain Invaders” experiment.

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

P300 dataset BI2014a from a “Brain Invaders” experiment

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

P300 dataset BI2014a 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
Number of repetitions: 12

Data Structure

Trials: variable; up to 8 attempts per level, 9 levels per session
Blocks per session: 9
Trials context: 9 levels per session, up to 8 attempts per level to destroy target

Preprocessing

Data state: raw EEG with hardware tagging (USB digital-to-analog converter for synchronization)
Preprocessing applied: False
Notes: No digital filter applied during recording. USB digital-to-analog converter used to reduce jitter and synchronize experimental tags with EEG signals.

Signal Processing

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

Cross-Validation

Evaluation type: cross_session

Performance (Original Study)

Note: Real-time adaptive RMDM classifier used for assessing participants’ command with calibration-free procedure

BCI Application

Applications: gaming
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual
Type: Perception

Documentation

Description: Dataset contains electroencephalographic (EEG) recordings of 71 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame 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 the P300 response.
DOI: 10.5281/zenodo.3266223
Associated paper DOI: hal-02171575
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.3266223
Publication year: 2019
Ethics approval: Approved by the Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle)
Acknowledgements: At the end of the experiment one ticket of cinema was offered to each subject, for a value of 7.5 euros per subject.
Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface, Experiment, Collaboration, Multi-User, Hyperscanning

Abstract

We describe the experimental procedures for the bi2014a dataset that contains electroencephalographic (EEG) recordings of 71 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame 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 the P300 response. EEG data were recorded using 16 active dry electrodes with up to three game sessions. The experiment took place at GIPSA-lab, Grenoble, France, in 2014.

Methodology

The experiment was designed to study the viability of a calibration-less P300-based BCI system with dry electrodes. Visual P300 is an event-related potential (ERP) elicited by an expected but unpredictable target visual stimulation (oddball paradigm), with peaking amplitude 240-600 ms after stimulus onset. Two event-related stimuli: Target (P300 expected) and Non-Target (no P300). The experiment used Brain Invaders, a P300-based BCI open-source software. A repetition is composed of 12 flashes (one for each group), of which two include the Target symbol (Target flashes) and 10 do not (non-Target flashes). The ratio of Target versus non-Target epochs in the whole datasets is one-to-five. During the experiment, the output of a real-time adaptive Riemannian Minimum Distance to Mean (RMDM) classifier was used for assessing the participants’ command. Game session was compounded by nine levels, consisting in a unique and predefined configuration of the 36 symbols of the interface. Players had up to eight attempts to destroy the target symbol. If the player missed all eight attempts, the level was started once again from the beginning. Average duration of five minutes for the nine levels. Experimenter could end the experiment if no control over the BCI system was gained after 10 minutes.

References

Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (BI2014a). https://hal.archives-ouvertes.fr/hal-02171575 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=64, range 24–24 yr, mean 23.0 yr)

20

Other · 64

Channel counts: 16 ch (n=64 recordings)

Sampling frequencies: 512.0 Hz (n=64 recordings)

Total recording duration: 12 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 512 Hz · 64 subjects, 64 recordings

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

Showing one representative recording out of 64 subjects and 64 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.NM000244(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Korczowski2014_P300_BI2014a

Canonical—

Importable asNM000244 · Korczowski2014_P300_BI2014a

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

class eegdash.dataset.NM000244(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P300 dataset BI2014a from a “Brain Invaders” experiment

Study:: nm000244 (NeMAR)
Author (year):: Korczowski2014_P300_BI2014a
Canonical:: —

Also importable as: NM000244, Korczowski2014_P300_BI2014a.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 64; recordings: 64; 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/nm000244 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000244

Examples

>>> from eegdash.dataset import NM000244
>>> dataset = NM000244(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 — NM000244.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 nm000244 to reproduce the tutorial on this dataset.

Citation

Louis Korczowski, Ekaterina Ostaschenko, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, … (2019). P300 dataset BI2014a 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

NM000215EEG · 38 subjSame authors NM000217EEG · 44 subjSame authors NM000201EEG · 24 subj NM000206EEG · 15 subj NM000343EEG · 15 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	`NM000244`
Title	P300 dataset BI2014a from a “Brain Invaders” experiment
Author (year)	`Korczowski2014_P300_BI2014a`
Canonical	—
Importable as	`NM000244`, `Korczowski2014_P300_BI2014a`
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

NM000244: eeg dataset, 64 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#