NM000216: eeg dataset, 43 subjects#

P300 dataset BI2015a from a “Brain Invaders” experiment

Access recordings and metadata through EEGDash.

Citation: Louis Korczowski, Martine Cederhout, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, Marco Congedo (2019). P300 dataset BI2015a from a “Brain Invaders” experiment.

Modality: eeg Subjects: 43 Recordings: 129 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000216

dataset = NM000216(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000216(cache_dir="./data", subject="01")

Advanced query

dataset = NM000216(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000216,
  title = {P300 dataset BI2015a from a "Brain Invaders" experiment},
  author = {Louis Korczowski and Martine Cederhout and Anton Andreev and Grégoire Cattan and Pedro Luiz Coelho Rodrigues and Violette Gautheret and Marco Congedo},
}

About This Dataset#

P300 dataset BI2015a from a “Brain Invaders” experiment

P300 dataset BI2015a from a “Brain Invaders” experiment.

Dataset Overview

View full README

P300 dataset BI2015a from a “Brain Invaders” experiment

P300 dataset BI2015a from a “Brain Invaders” experiment.

Dataset Overview

  • Code: BrainInvaders2015a

  • Paradigm: p300

  • DOI: https://doi.org/10.5281/zenodo.3266929

  • Subjects: 43

  • Sessions per subject: 3

  • 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: 10-10

  • Hardware: g.USBamp (g.tec, Schiedlberg, Austria)

  • Software: OpenVibe

  • Reference: right earlobe

  • Ground: Fz

  • Sensor type: wet electrodes

  • Line frequency: 50.0 Hz

  • Online filters: no digital filter applied

  • Cap manufacturer: g.tec

  • Cap model: g.GAMMAcap

  • Electrode type: wet

  • Electrode material: Silver/Silver Chloride

Participants

  • Number of subjects: 43

  • Health status: healthy

  • Age: mean=23.7, std=3.19

  • Gender distribution: male=31, female=12

  • BCI experience: mostly students and young researchers

Experimental Protocol

  • Paradigm: p300

  • Task type: target detection

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Study design: calibration-less P300-based BCI with modulation of flash duration; three game sessions (9 levels each) with different flash durations (110ms, 80ms, 50ms); resting state and eyes closed recorded before and after sessions; subjects instructed to limit eye blinks, head movements and face muscular contractions

  • Feedback type: visual (game interface with real-time adaptive Riemannian RMDM classifier)

  • Stimulus type: oddball paradigm on grid of 36 symbols (1 Target, 35 Non-Target) flashed pseudo-randomly

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: False

  • Instructions: destroy target symbol within 8 attempts; aliens move slowly and regularly according to predefined path to maintain attention

  • Stimulus presentation: SoftwareName=OpenViBE

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

  • Number of repetitions: 12

Data Structure

  • Trials: variable per subject (up to 8 attempts per level, 9 levels per session, 3 sessions)

  • Blocks per session: 3

  • Trials context: 9 levels per session with variable duration (average ~5 minutes per session, max 10 minutes)

Preprocessing

  • Data state: raw EEG with synchronized USB tagging (reduced jitter using USB digital-to-analog converter)

  • Preprocessing applied: False

  • Notes: no digital filter applied during acquisition; tags synchronized with EEG signals to reduce jitter; consistent tagging latency across Brain Invaders databases

Signal Processing

  • Classifiers: Riemannian Minimum Distance to Mean (RMDM), adaptive

  • Feature extraction: Covariance/Riemannian

Cross-Validation

  • Evaluation type: cross_session

BCI Application

  • Applications: gaming

  • Environment: small room (4 square meters) with 24 inch screen

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Documentation

  • DOI: 10.5281/zenodo.3266930

  • Associated paper DOI: hal-02172347

  • License: CC-BY-4.0

  • Investigators: Louis Korczowski, Martine Cederhout, 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.3266930

  • Publication year: 2019

  • Ethics approval: Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle)

  • How to acknowledge: Korczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P.L.C., Gautheret, V., Congedo, M. (2019). Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a). Technical Report, GIPSA-lab.

  • Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface, Experiment

Abstract

This dataset contains electroencephalographic (EEG) recordings of 50 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 32 active wet electrodes with three conditions: flash duration 50ms, 80ms or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015.

Methodology

The experiment was designed to study the influence of the flash duration on a calibration-less P300-based BCI system with wet electrodes and as a screening session for potential candidates for a broader multi-user BCI study. The 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. During the experiment, the output of a real-time adaptive Riemannian Minimum Distance to Mean (RMDM) classifier was used for assessing the participants’ command. This scheme allows a calibration-free classifier. Before and after the three game sessions, around one minute of resting state and eyes closed conditions were recorded. The interface of Brain Invaders is composed of 36 aliens. In the Brain Invaders P300 paradigm, a repetition is composed of 12 flashes of pseudo-random groups of six symbols chosen in such a way that after each repetition each symbol has flashed exactly two times. A game session was compounded by nine levels, consisting in a unique and predefined configuration of the 36 symbols of the interface. Aliens slowly and regularly moved according to a predefined path keeping constant the inter-distance between adjacent aliens to maintain high player’s attention during the whole experiment.

References

Korczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (BI2015a) https://hal.archives-ouvertes.fr/hal-02172347 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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000216

Title

P300 dataset BI2015a from a “Brain Invaders” experiment

Author (year)

Korczowski2015_P300

Canonical

BrainInvaders2015a, BI2015a

Importable as

NM000216, Korczowski2015_P300, BrainInvaders2015a, BI2015a

Year

2019

Authors

Louis Korczowski, Martine Cederhout, 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: 43

  • Recordings: 129

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 512.0

  • Duration (hours): 11.664148763020831

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 1.9 GB

  • File count: 129

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000216 class to access this dataset programmatically.

class eegdash.dataset.NM000216(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

P300 dataset BI2015a from a “Brain Invaders” experiment

Study:

nm000216 (NeMAR)

Author (year):

Korczowski2015_P300

Canonical:

BrainInvaders2015a, BI2015a

Also importable as: NM000216, Korczowski2015_P300, BrainInvaders2015a, BI2015a.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 43; recordings: 129; 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/nm000216 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000216

Examples

>>> from eegdash.dataset import NM000216
>>> dataset = NM000216(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#