NM000260: eeg dataset, 23 subjects#

Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)

Access recordings and metadata through EEGDash.

Citation: G.F.P. Van Veen, A. Barachant, A. Andreev, G. Cattan, P. Rodrigues, M. Congedo (2019). Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012). 10.5281/zenodo.2649006

Modality: eeg Subjects: 23 Recordings: 46 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000260

dataset = NM000260(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 = NM000260(cache_dir="./data", subject="01")

Advanced query

dataset = NM000260(
    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{nm000260,
  title = {Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)},
  author = {G.F.P. Van Veen and A. Barachant and A. Andreev and G. Cattan and P. Rodrigues and M. Congedo},
  doi = {10.5281/zenodo.2649006},
  url = {https://doi.org/10.5281/zenodo.2649006},
}

About This Dataset#

BrainInvaders2012

P300 dataset BI2012 from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2012 Paradigm: p300 DOI: https://doi.org/10.5281/zenodo.2649006

View full README

BrainInvaders2012

P300 dataset BI2012 from a “Brain Invaders” experiment.

Dataset Overview

Code: BrainInvaders2012 Paradigm: p300 DOI: https://doi.org/10.5281/zenodo.2649006 Subjects: 25 Sessions per subject: 1 Events: Target=2, NonTarget=1 Trial interval: [0, 1] s Runs per session: 2 Session IDs: 0 File format: mat, csv Contributing labs: GIPSA-lab

Acquisition

Sampling rate: 128.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: F7, F3, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2 Montage: standard_1020 Hardware: NeXus-32 (MindMedia/TMSi) Software: OpenVibe Reference: hardware common average reference Ground: FZ Sensor type: EEG Line frequency: 50.0 Hz Electrode type: wet Electrode material: Silver/Silver Chloride

Participants

Number of subjects: 25 Health status: healthy Age: mean=24.4, std=2.76, min=21, max=31 BCI experience: half played games occasionally (around 4.5 hours a week) Species: human

Experimental Protocol

Paradigm: p300 Task type: brain_invaders Number of classes: 2 Class labels: Target, NonTarget Study design: longitudinal and transversal design with training-test mode of operation Feedback type: visual (game interface) Stimulus type: visual flashes of alien groups Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: both Training/test split: True Instructions: limit eye blinks, head movements and face muscular contractions; silently count the number of Target flashes Stimulus presentation: repetition_structure=12 flashes per repetition (2 Target, 10 non-Target), flash_groups=12 groups of 6 aliens (36 total aliens), target_ratio=1:5 (Target to non-Target), screen_distance=75 to 115 cm

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 repetitions: 8

Data Structure

Trials: {‘Target’: 128, ‘non-Target’: 640} Trials per class: Target=128, non-Target=640 Trials context: per session (Training session); variable in Online session depending on user performance

Preprocessing

Data state: raw EEG with software tagging (note: tagging introduces jitter and latency) Preprocessing applied: False Notes: Software tagging introduces a jitter and a latency which artificially modify the ERPs onset. Strong drift over time resulting in higher jitter. Only possible to compare ERP acquired within the same experimental conditions when latency is not corrected.

Signal Processing

Classifiers: xDAWN, Riemannian Feature extraction: Covariance/Riemannian, xDAWN Spatial filters: xDAWN

Performance (Original Study)

BCI Application

Applications: gaming Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Visual Type: Perception

Documentation

Description: EEG recordings of 25 subjects testing the Brain Invaders, a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders DOI: 10.5281/zenodo.2649006 Associated paper DOI: 10.5281/zenodo.2649006 License: CC-BY-4.0 Investigators: G.F.P. Van Veen, A. Barachant, A. Andreev, G. Cattan, P. Rodrigues, 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.2649006 Publication year: 2019 Acknowledgements: All subjects were volunteers recruited by means of flyers and of the mailing list of the University of Grenoble-Alpes. All participants provided written informed consent confirming the notification of the experimental process, the data management procedures and the right to withdraw from the experiment at any moment. Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface, Experiment

Abstract

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2649006 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the Brain Invaders (1), a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab, Grenoble, France, in 2012 (2,3). Python code for manipulating the data is available at plcrodrigues/py.BI.EEG.2012-GIPSA. The ID of this dataset is BI.EEG.2012-GIPSA.

Methodology

The visual P300 is an event-related potential (ERP) elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. The experiment features a training-test mode of operation and both a longitudinal and transversal design. Training session: Target alien chosen randomly at each repetition, 8 Targets total, 8 repetitions each, resulting in 128 Target trials and 640 non-Target flashes. Online session: consisted of three levels with different distractor configurations, minimum 3.5 minutes per level, counter-balanced order across subjects. Interface: 36 aliens flashing in 12 groups of 6, each repetition has 12 flashes (2 Target, 10 non-Target). P300 peak latency: 240-600 ms post-stimulus.

References

Van Veen, G., Barachant, A., Andreev, A., Cattan, G., Rodrigues, P. C., & Congedo, M. (2019). Building Brain Invaders: EEG data of an experimental validation. arXiv preprint arXiv:1905.05182. 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	`NM000260`
Title	Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)
Author (year)	`BrainInvaders2012`
Canonical	—
Importable as	`NM000260`, `BrainInvaders2012`
Year	2019
Authors	G.F.P. Van Veen, A. Barachant, A. Andreev, G. Cattan, P. Rodrigues, M. Congedo
License	CC-BY-4.0
Citation / DOI	doi:10.5281/zenodo.2649006
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: 23
Recordings: 46
Tasks: 1

Channels & sampling rate

Channels: 17
Sampling rate (Hz): 128.0
Duration (hours): 7.122400173611111

Electrode Layout#

Electrode layout — EEG · 17 sensors — 17 channels

Dataset Statistics#

Age distribution (n=23, range 24–24 yr)

20

Channel counts: 17 ch (n=46 recordings)

Sampling frequencies: 128.0 Hz (n=46 recordings)

Total recording duration: 7 h 7 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 NM000260 class to access this dataset programmatically.

class eegdash.dataset.NM000260(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Van Veen, Barachant & Andreev 2012 — Building Brain Invaders: EEG data of an experimental validation (BI2012)

Study:: nm000260 (NeMAR)
Author (year):: BrainInvaders2012
Canonical:: —

Also importable as: NM000260, BrainInvaders2012.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 23; recordings: 46; 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/nm000260 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000260 DOI: https://doi.org/10.5281/zenodo.2649006

Examples

>>> from eegdash.dataset import NM000260
>>> dataset = NM000260(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)