EEGdash›NeMAR›NM000262

Iss. 262 · 19 subjects · 280 recordings · CC0

Dataset Brief · P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

NM000262: eeg dataset, 19 subjects#

P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

Access recordings and metadata through EEGDash.

Citation: J. David Chailloux Peguero, Omar Mendoza-Montoya, Javier M. Antelis (20). P300 BCI EEG dataset (Chailloux Peguero et al. 2020). 10.82901/nemar.nm000262

Modality: eeg Subjects: 19 Recordings: 280 License: CC0 Source: nemar

Metadata: Complete (100%)

19-participant EEG dataset — P300 BCI EEG dataset (Chailloux Peguero et al. 2020).

Data & curation J. David Chailloux Peguero · Omar Mendoza-Montoya · Javier M. Antelis
Senior author Javier M. Antelis
Year 20 · Distributed via NeMAR

EEG · 8 ch256 HzBIDS 1.9.0Task · p3003 sessions

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 NM000262

dataset = NM000262(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 = NM000262(cache_dir="./data", subject="01")

Advanced query

dataset = NM000262(
    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{nm000262,
  title = {P300 BCI EEG dataset (Chailloux Peguero et al. 2020)},
  author = {J. David Chailloux Peguero and Omar Mendoza-Montoya and Javier M. Antelis},
  doi = {10.82901/nemar.nm000262},
  url = {https://doi.org/10.82901/nemar.nm000262},
}

§ 02Study · The README

About This Dataset#

A P300 brain-computer interface dataset comprising EEG recordings from 19 healthy participants across 3 sessions each. The study investigates how visual stimulation conditions (standard flash vs. cartoon face stimuli) affect P300-BCI performance using an 8-channel montage sampled at 256 Hz. Data were collected using a P300 paradigm with 5 grid sizes (4-9 symbols) and include target and non-target event classifications.

EEG data were acquired using a g.USBamp amplifier (g.tec) with 8 passive g.SCARABEO electrodes positioned at Fz, Cz, P3, Pz, P4, PO7, PO8, and Oz according to the standard 10-20 montage. Sampling rate was 256 Hz with right earlobe reference and AFz ground. Line frequency was 60 Hz. The P300 paradigm employed visual stimulation with two stimulus types (standard flash and cartoon face) across 5 grid sizes (4-9 symbols), with an inter-stimulus interval of 75 ms and stimulus onset asynchrony of 150 ms.

DOI

P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000262-blue)](https://doi.org/10.82901/nemar.nm000262) # P300 BCI EEG dataset (Chailloux Peguero et al. 2020) ## Overview A P300 brain-computer interface dataset comprising EEG recordings from 19 healthy participants across 3 sessions each. The study investigates how visual stimulation conditions (standard flash vs. cartoon face stimuli) affect P300-BCI performance using an 8-channel montage sampled at 256 Hz. Data were collected using a P300 paradigm with 5 grid sizes (4-9 symbols) and include target and non-target event classifications. ## Dataset Summary | Property | Value | |---|—| | Subjects | 19 | | Channels | 8 | | Classes | 2 | | Trial length | 1 s | | Sampling frequency | 256 Hz | | Sessions | 3 | | Paradigm | P300 | ## Data Collection Methods EEG data were acquired using a g.USBamp amplifier (g.tec) with 8 passive g.SCARABEO electrodes positioned at Fz, Cz, P3, Pz, P4, PO7, PO8, and Oz according to the standard 10-20 montage. Sampling rate was 256 Hz with right earlobe reference and AFz ground. Line frequency was 60 Hz. The P300 paradigm employed visual stimulation with two stimulus types (standard flash and cartoon face) across 5 grid sizes (4-9 symbols), with an inter-stimulus interval of 75 ms and stimulus onset asynchrony of 150 ms. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Chailloux2020 from moabb.paradigms import P300 paradigm = P300() dataset = Chailloux2020() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Chailloux2020.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.3390/s20247198](https://doi.org/10.3390/s20247198) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC0

Authors:

1. David Chailloux Peguero
Omar Mendoza-Montoya
Javier M. Antelis

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000262

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=19, range 25–25 yr, mean 25.0 yr)

25

Other · 19

Channel counts: 8 ch (n=280 recordings)

Sampling frequencies: 256.0 Hz (n=280 recordings)

Total recording duration: 31 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 8 ch · EEG · 256 Hz · 19 subjects, 280 recordings

Live trace viewer — sub-1 · ses-0 · task-p300 · run-0

Showing one representative recording out of 19 subjects and 280 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 · 8 sensors — 8 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.NM000262(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000262

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

class eegdash.dataset.NM000262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P300 BCI EEG dataset (Chailloux Peguero et al. 2020)

Study:: nm000262 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000262.

Modality: eeg; Subject type: Unknown. Subjects: 19; recordings: 280; 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/nm000262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000262 DOI: https://doi.org/10.82901/nemar.nm000262

Examples

>>> from eegdash.dataset import NM000262
>>> dataset = NM000262(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 — NM000262.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 nm000262 to reproduce the tutorial on this dataset.

Citation

J. David Chailloux Peguero, Omar Mendoza-Montoya, Javier M. Antelis (20). P300 BCI EEG dataset (Chailloux Peguero et al. 2020). 10.82901/nemar.nm000262

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000262.

Related & sibling datasets

ON003190EEG · 19 subj ON004350EEG · 24 subj ON002158EEG · 20 subj NM000269EEG · 13 subj ON004841EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.82901/nemar.nm000262

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000262`
Title	P300 BCI EEG dataset (Chailloux Peguero et al. 2020)
Author (year)	—
Canonical	—
Importable as	`NM000262`
Year	20
Authors	David Chailloux Peguero, Omar Mendoza-Montoya, Javier M. Antelis
License	CC0
Citation / DOI	10.82901/nemar.nm000262
Source links	OpenNeuro \| NeMAR \| Source URL

NM000262: eeg dataset, 19 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#