NM000264: eeg dataset, 24 subjects#

Name: Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)
Published: 2019-01-01
License: CC-BY-1.0

Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)

Access recordings and metadata through EEGDash.

Citation: E. Vaineau, A. Barachant, A. Andreev, P. Rodrigues, G. Cattan, M. Congedo (2019). Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a). 10.5281/zenodo.1494163

Modality: eeg Subjects: 24 Recordings: 292 License: CC-BY-1.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000264

dataset = NM000264(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 = NM000264(cache_dir="./data", subject="01")

Advanced query

dataset = NM000264(
    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{nm000264,
  title = {Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)},
  author = {E. Vaineau and A. Barachant and A. Andreev and P. Rodrigues and G. Cattan and M. Congedo},
  doi = {10.5281/zenodo.1494163},
  url = {https://doi.org/10.5281/zenodo.1494163},
}

About This Dataset#

BrainInvaders2013a

P300 dataset BI2013a from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2013a Paradigm: p300 DOI: https://doi.org/10.5281/zenodo.2669187

View full README

BrainInvaders2013a

P300 dataset BI2013a from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2013a Paradigm: p300 DOI: https://doi.org/10.5281/zenodo.2669187 Subjects: 24 Sessions per subject: 8 Events: Target=33285, NonTarget=33286 Trial interval: [0, 1] s Runs per session: 2 File format: mat, csv, gdf Contributing labs: GIPSA-lab

Acquisition

Sampling rate: 512.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: Fp1, Fp2, F5, AFz, F6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2 Montage: standard_1020 Hardware: g.USBamp (g.tec, Schiedlberg, Austria) Software: OpenVibe Reference: left 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: 24 Health status: healthy Age: mean=25.96, std=4.46, min=20.0, max=30.0 Gender distribution: male=12, female=12 BCI experience: volunteers recruited via flyers and university mailing list Species: human

Experimental Protocol

Paradigm: p300 Task type: visual P300 BCI Number of classes: 2 Class labels: Target, NonTarget Study design: compare P300-based BCI with and without adaptive calibration using Riemannian geometry; randomised order of runs (adaptive vs non-adaptive) Feedback type: visual (Brain Invaders video game interface) Stimulus type: visual flashes Stimulus modalities: visual Primary modality: visual Mode: both Training/test split: True Instructions: destroy targets in Brain Invaders BCI video game Stimulus presentation: distance_from_screen=75 to 115 cm, screen=ViewSonic 22 inch, flash_groups=36 symbols distributed in 12 groups

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

Data Structure

Trials: {‘Training_Target’: 80, ‘Training_non-Target’: 400, ‘Online’: ‘variable (depends on user performance)’} Trials context: per_phase

Preprocessing

Data state: raw EEG with software tagging via USB (note: tagging introduces jitter and latency) Preprocessing applied: False Notes: Tags sent by application to amplifier through USB port and recorded as supplementary channel; tagging process identical in all experimental conditions

Signal Processing

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

Cross-Validation

Evaluation type: cross_session

Performance (Original Study)

Balanced Accuracy: used due to unbalanced classes (1:5 ratio Target to non-Target)

BCI Application

Applications: gaming Environment: small room (4 square meters) with one-way glass window for experimenter observation Online feedback: True

Tags

Pathology: Healthy Modality: Visual Type: Perception

Documentation

Description: EEG recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment comparing adaptive vs non-adaptive calibration using Riemannian geometry DOI: 10.5281/zenodo.1494163 Associated paper DOI: 10.5281/zenodo.2649006 License: CC-BY-1.0 Investigators: E. Vaineau, A. Barachant, A. Andreev, P. Rodrigues, G. Cattan, M. Congedo Senior author: M. 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.1494163 Publication year: 2019 Ethics approval: Approved by the Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle) Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface, Experiment, Adaptive, Calibration

Abstract

This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed to compare the use of a P300-based brain-computer interface with and without adaptive calibration using Riemannian geometry. EEG data were recorded using 16 electrodes during an experiment at GIPSA-lab, Grenoble, France, in 2013.

Methodology

Subjects participated in sessions with two runs (Non-Adaptive and Adaptive, randomised order). Each run had Training (calibration) and Online phases. In Non-Adaptive mode, Training data calibrated the MDM classifier for Online phase. In Adaptive mode, classifier initialized with generic class geometric means from previous experiment and continuously adapted using Riemannian method. Brain Invaders interface: 36 symbols in 12 groups, one repetition = 12 flashes (2 Target, 10 non-Target). Training phase: 80 Target and 400 non-Target flashes (fixed). Online phase: variable repetitions based on performance to destroy targets. Subjects blind to mode of operation.

References

Vaineau, E., Barachant, A., Andreev, A., Rodrigues, P. C., Cattan, G. & Congedo, M. (2019). Brain invaders adaptive versus non-adaptive P300 brain-computer interface dataset. arXiv preprint arXiv:1904.09111. Barachant A, Congedo M (2014) A Plug & Play P300 BCI using Information Geometry. arXiv:1409.0107. Congedo M, Goyat M, Tarrin N, Ionescu G, Rivet B,Varnet L, Rivet B, Phlypo R, Jrad N, Acquadro M, Jutten C (2011) “Brain Invaders”: a prototype of an open-source P300-based video game working with the OpenViBE platform. Proc. IBCI Conf., Graz, Austria, 280-283. 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	`NM000264`
Title	Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)
Author (year)	`BrainInvaders2013`
Canonical	—
Importable as	`NM000264`, `BrainInvaders2013`
Year	2019
Authors	Vaineau, A. Barachant, A. Andreev, P. Rodrigues, G. Cattan, M. Congedo
License	CC-BY-1.0
Citation / DOI	doi:10.5281/zenodo.1494163
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: 24
Recordings: 292
Tasks: 1

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 512.0
Duration (hours): 20.632897135416663

Electrode Layout#

Electrode layout — EEG · 16 sensors — 16 channels

Dataset Statistics#

Age distribution (n=24, range 25–25 yr)

25

Channel counts: 16 ch (n=292 recordings)

Sampling frequencies: 512.0 Hz (n=292 recordings)

Total recording duration: 20 h 37 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 NM000264 class to access this dataset programmatically.

class eegdash.dataset.NM000264(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Vaineau, Barachant & Andreev 2013 — Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset (BI2013a)

Study:: nm000264 (NeMAR)
Author (year):: BrainInvaders2013
Canonical:: —

Also importable as: NM000264, BrainInvaders2013.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 24; recordings: 292; 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/nm000264 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000264 DOI: https://doi.org/10.5281/zenodo.1494163

Examples

>>> from eegdash.dataset import NM000264
>>> dataset = NM000264(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)