NM000217: eeg dataset, 44 subjects#

P300 dataset BI2015b 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 BI2015b from a “Brain Invaders” experiment.

Modality: eeg Subjects: 44 Recordings: 176 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000217

dataset = NM000217(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000217(cache_dir="./data", subject="01")

Advanced query

dataset = NM000217(
    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{nm000217,
  title = {P300 dataset BI2015b 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 BI2015b from a “Brain Invaders” experiment

P300 dataset BI2015b from a “Brain Invaders” experiment.

Dataset Overview

View full README

P300 dataset BI2015b from a “Brain Invaders” experiment

P300 dataset BI2015b from a “Brain Invaders” experiment.

Dataset Overview

  • Code: BrainInvaders2015b

  • Paradigm: p300

  • DOI: https://doi.org/10.5281/zenodo.3267307

  • Subjects: 44

  • Sessions per subject: 1

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 1] s

  • Runs per session: 4

  • File format: mat and csv

  • Contributing labs: GIPSA-lab

Acquisition

  • Sampling rate: 512.0 Hz

  • Number of channels: 32

  • Channel types: eeg=32

  • Channel names: AFz, C3, C4, CP1, CP2, CP5, CP6, Cz, F3, F4, F7, F8, FC1, FC2, FC5, FC6, Fp1, Fp2, O1, O2, Oz, P3, P4, P7, P8, PO10, PO7, PO8, PO9, Pz, T7, T8

  • Montage: 10-10

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

  • Software: OpenVibe

  • Reference: right earlobe

  • Ground: Fz

  • Sensor type: wet Silver/Silver Chloride 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: 44

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=23.7, std=3.19

  • Gender distribution: male=36, female=14

  • BCI experience: mostly students and young researchers

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Study design: Three game sessions with different flash durations (110ms, 80ms, 50ms), with resting state and eyes closed conditions recorded before and after. Subjects were instructed to limit eye blinks, head movements and face muscular contractions.

  • Feedback type: visual (game interface with reward screen)

  • Stimulus type: visual flash

  • Stimulus modalities: visual

  • Primary modality: visual

  • Mode: online

  • Training/test split: False

  • Instructions: Players had up to eight attempts to destroy the target symbol per level. Target symbol identification using oddball paradigm with 36 aliens flashing in pseudo-random groups of six symbols.

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: 9

  • Trials context: per session (9 levels per session, 3 sessions with different flash durations)

Preprocessing

  • Data state: raw EEG with synchronized hardware tagging via USB digital-to-analog converter (reduced jitter compared to software tagging)

  • Preprocessing applied: False

  • Notes: Data were stored with no digital filter applied. USB digital-to-analog converter connected to the g.USBamp trigger channel was used to synchronize experimental tags produced by Brain Invaders with EEG signals to reduce jitter.

Signal Processing

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

  • Feature extraction: Covariance/Riemannian, xDAWN

Cross-Validation

  • Evaluation type: cross_session

Performance (Original Study)

  • Note: Real-time adaptive classifier used during experiment, performance variable per subject

BCI Application

  • Applications: gaming

  • Environment: small room with a surface of four meters square, containing a 24’ screen

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Documentation

  • Description: 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. Three conditions: flash duration 50ms, 80ms or 110ms.

  • 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: France

  • 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)

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

Abstract

We describe the experimental procedures for an experiment dataset that we have made publicly available at https://doi.org/10.5281/zenodo.3266930 in mat and csv formats. 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 consisted of three game sessions of Brain Invaders of 9 levels each with different flash duration (110ms, 80ms, 50ms). Before and after the three game sessions, around one minute of resting state and eyes closed conditions were recorded. The interface is composed of 36 aliens. 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. The ratio of Target versus non-Target 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. This scheme allows a calibration-free classifier.

References

Korczowski, L., Cederhout, M., Andreev, A., Cattan, G., Rodrigues, P. L. C., Gautheret, V., & Congedo, M. (2019). Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (BI2015b) 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

NM000217

Title

P300 dataset BI2015b from a “Brain Invaders” experiment

Author (year)

Korczowski2015_P300_BI2015b

Canonical

BrainInvaders2015b, BI2015b

Importable as

NM000217, Korczowski2015_P300_BI2015b, BrainInvaders2015b, BI2015b

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: 44

  • Recordings: 176

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 512.0

  • Duration (hours): 26.080008680555554

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 4.3 GB

  • File count: 176

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000217 class to access this dataset programmatically.

class eegdash.dataset.NM000217(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

P300 dataset BI2015b from a “Brain Invaders” experiment

Study:

nm000217 (NeMAR)

Author (year):

Korczowski2015_P300_BI2015b

Canonical:

BrainInvaders2015b, BI2015b

Also importable as: NM000217, Korczowski2015_P300_BI2015b, BrainInvaders2015b, BI2015b.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 44; recordings: 176; 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/nm000217 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000217

Examples

>>> from eegdash.dataset import NM000217
>>> dataset = NM000217(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#