EEGdash›NeMAR›NM000202

Iss. 202 · 25 subjects · 25 recordings · CC-BY-4.0

Dataset Brief · P300 dataset BI2012 from a "Brain Invaders" experiment

NM000202: eeg dataset, 25 subjects#

P300 dataset BI2012 from a “Brain Invaders” experiment

Citation: G.F.P. Van Veen, A. Barachant, A. Andreev, G. Cattan, P. Rodrigues, M. Congedo (2019). P300 dataset BI2012 from a “Brain Invaders” experiment.

25-participant EEG dataset — P300 dataset BI2012 from a "Brain Invaders" experiment.

Data & curation G.F.P. Van Veen · A. Barachant · A. Andreev · G. Cattan · P. Rodrigues · M. Congedo
Year 2019 · Distributed via NeMAR

EEG · 16 ch128 HzBIDS 1.9.0Task · p300

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000202

dataset = NM000202(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 = NM000202(cache_dir="./data", subject="01")

Advanced query

dataset = NM000202(
    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{nm000202,
  title = {P300 dataset BI2012 from a "Brain Invaders" experiment},
  author = {G.F.P. Van Veen and A. Barachant and A. Andreev and G. Cattan and P. Rodrigues and M. Congedo},
}

§ 02Study · The README

About This Dataset#

P300 dataset BI2012 from a “Brain Invaders” experiment.

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

P300 dataset BI2012 from a “Brain Invaders” experiment

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

P300 dataset BI2012 from a “Brain Invaders” experiment

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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=25, range 24–24 yr, mean 24.0 yr)

20

Other · 25

Channel counts: 16 ch (n=25 recordings)

Sampling frequencies: 128.0 Hz (n=25 recordings)

Total recording duration: 2 h 30 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 128 Hz · 25 subjects, 25 recordings

Live trace viewer — sub-13 · ses-0 · task-p300 · run-0

Showing one representative recording out of 25 subjects and 25 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 16 sensors — 16 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000202(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000202

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000202(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P300 dataset BI2012 from a “Brain Invaders” experiment

Study:: nm000202 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: NM000202, nan.

Modality: eeg. Subjects: 25; recordings: 25; 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/nm000202 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000202

Examples

>>> from eegdash.dataset import NM000202
>>> dataset = NM000202(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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000202.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000202 to reproduce the tutorial on this dataset.

Citation

G.F.P. Van Veen, A. Barachant, A. Andreev, G. Cattan, P. Rodrigues, … (2019). P300 dataset BI2012 from a "Brain Invaders" experiment.

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

Related & sibling datasets

ON003801EEG · 20 subj ON003194EEG · 15 subj ON004661EEG · 17 subj ON007262EEG · 18 subj NM000154EEG · 24 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000202`
Title	P300 dataset BI2012 from a “Brain Invaders” experiment
Author (year)	—
Canonical	—
Importable as	`NM000202`
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	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000202: eeg dataset, 25 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#