NM000215: eeg dataset, 38 subjects#

P300 dataset BI2014b from a “Brain Invaders” experiment

Access recordings and metadata through EEGDash.

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.

Modality: eeg Subjects: 38 Recordings: 38 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset#

P300 dataset BI2014b from a “Brain Invaders” experiment

P300 dataset BI2014b from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2014b
Paradigm: p300
DOI: https://doi.org/10.5281/zenodo.3267301

View full README

P300 dataset BI2014b from a “Brain Invaders” experiment

P300 dataset BI2014b from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2014b
Paradigm: p300
DOI: https://doi.org/10.5281/zenodo.3267301
Subjects: 38
Sessions per subject: 1
Events: Target=2, NonTarget=1
Trial interval: [0, 1] s
File format: mat and csv

Acquisition

Sampling rate: 512.0 Hz
Number of channels: 32
Channel types: eeg=32
Channel names: Fp1, Fp2, AFz, F7, F3, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO7, O1, Oz, O2, PO8, PO9, PO10
Montage: standard_1010
Hardware: g.USBamp (g.tec, Schiedlberg, Austria)
Software: OpenVibe
Reference: right earlobe
Ground: Fz
Sensor type: wet electrodes
Line frequency: 50.0 Hz
Cap manufacturer: g.tec
Cap model: g.GAMMAcap
Electrode type: wet
Electrode material: Ag/AgCl

Participants

Number of subjects: 38
Health status: healthy
Age: mean=24.1, std=3.09
Gender distribution: male=24, female=14
BCI experience: not naïve users - selected on the basis of their individual score during a preliminary session of Brain Invaders
Species: human

Experimental Protocol

Paradigm: p300
Task type: oddball
Number of classes: 2
Class labels: Target, NonTarget
Study design: multi-user/hyperscanning experiment with three randomized conditions (Solo1, Solo2, Collaboration). Subjects played in pairs. Solo conditions used a control design where non-playing participant focused on unanimated cross to prevent stimulus observation while EEG was recorded (to correct for fake inter-brain synchrony).
Study domain: inter-brain synchrony in collaborative BCI
Feedback type: visual
Stimulus type: visual flashes
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: online
Training/test split: False
Instructions: destroy the target alien symbol as fast as possible. Up to eight attempts per level. If all attempts missed, level restarted.
Stimulus presentation: repetition_structure=12 flashes per repetition of pseudo-random groups of 6 symbols, such that each symbol flashes exactly twice per repetition, target_ratio=1:5 (Target vs Non-Target), flash_groups=6 rows and 6 columns (pseudo-random groups, not physical arrangement), animation=aliens slowly and regularly moved according to predefined path with constant inter-distance

HED Event Annotations

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

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

Dataset Information#

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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 38
Recordings: 38
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 512.0
Duration (hours): 2.362566189236111

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=38, range 24–24 yr)

20

Channel counts: 32 ch (n=38 recordings)

Sampling frequencies: 512.0 Hz (n=38 recordings)

Total recording duration: 2 h 21 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000215 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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)