EEGdash›NeMAR›NM000194

Iss. 194 · 12 subjects · 24 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-010 RSVP P300 dataset

NM000194: eeg dataset, 12 subjects#

Name: BNCI 2015-010 RSVP P300 dataset
Published: 2013-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-010 RSVP P300 dataset

Citation: Laura Acqualagna, Benjamin Blankertz (2013). BNCI 2015-010 RSVP P300 dataset.

12-participant EEG dataset — BNCI 2015-010 RSVP P300 dataset.

Data & curation Laura Acqualagna · Benjamin Blankertz
Year 2013 · Distributed via NeMAR
Funding BMBF Grant · Grant Nos s · + 2 more

EEG · 63 (22), 61 (2) ch200 HzBIDS 1.9.0Task · p300HealthyVisualAttention

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 NM000194

dataset = NM000194(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 = NM000194(cache_dir="./data", subject="01")

Advanced query

dataset = NM000194(
    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{nm000194,
  title = {BNCI 2015-010 RSVP P300 dataset},
  author = {Laura Acqualagna and Benjamin Blankertz},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-010 RSVP P300 dataset.

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

BNCI 2015-010 RSVP P300 dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

BNCI 2015-010 RSVP P300 dataset

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: 30
Number of repetitions: 10
Stimulus onset asynchrony: 116.0 ms

Data Structure

Trials: 10 sequences of 30 symbols
Blocks per session: 3
Trials context: per sequence

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: lowpass filter, downsampling, baseline correction, artifact rejection
Lowpass filter: 40.0 Hz
Filter type: Chebyshev
Filter order: passband up to 40 Hz, stopband starting at 49 Hz
Artifact methods: min-max criterion for eye movement rejection (75 µV on F9, Fz, F10, AF3, AF4), broadband power rejection (5-40 Hz)
Re-reference: linked mastoids (offline)
Downsampled to: 200.0 Hz
Epoch window: [-0.1, 1.2]
Notes: Baseline correction on pre-stimulus interval (116ms for 116ms SOA, 83/2ms for 83ms SOA). Non-target epochs excluded if 3 preceding or following symbols were targets.

Signal Processing

Classifiers: LDA with shrinkage
Feature extraction: spatio-temporal features, averaged voltages within time windows
Frequency bands: alpha=[7, 13] Hz
Spatial filters: 55 channels used for classification (all except Fp1,2, AF3,4, F9,10, FT7,8)

Cross-Validation

Method: calibration/test split
Evaluation type: within_session

Performance (Original Study)

Accuracy: 94.8%
Mean Spelling Rate Symb Per Min: 1.43
Trial Duration 116Ms Soa S: 46.5
Trial Duration 83Ms Soa S: 36.6

BCI Application

Applications: speller, communication
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual
Type: ERP

Documentation

DOI: 10.1016/j.clinph.2012.12.050
License: CC-BY-NC-ND-4.0
Investigators: Laura Acqualagna, Benjamin Blankertz
Senior author: Benjamin Blankertz
Contact: laura.acqualagna@tu-berlin.de; benjamin.blankertz@tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Laboratory; Neurotechnology Group
Country: Germany
Repository: BNCI Horizon
Publication year: 2013
Funding: BMBF Grant; Grant Nos s; Grant No. MU MU; DFG Grant
Ethics approval: Study performed in accordance with the declaration of Helsinki
Keywords: Brain Computer Interfaces, RSVP, ERPs, Speller, P300, N2, gaze-independent

Abstract

A Brain Computer Interface (BCI) speller using rapid serial visual presentation (RSVP) paradigm for gaze-independent mental typewriting. Twelve healthy participants successfully operated the RSVP speller with mean online spelling rate of 1.43 symb/min and mean symbol selection accuracy of 94.8%. The RSVP speller does not require gaze shifts and can be operated by non-spatial visual attention, making it suitable for patients with impaired oculo-motor control.

Methodology

Three experimental conditions tested (NoColor 116ms, Color 116ms, Color 83ms SOA). Each condition included calibration, copy-spelling, and free-spelling phases. Vocabulary of 30 symbols presented one-by-one at screen center in pseudo-random order. EEG recorded at 1000 Hz with 63 channels, downsampled to 200 Hz for ERP analysis. Classification using LDA with shrinkage on spatio-temporal features from 5 individually selected time windows. Symbol selection based on averaged classifier output across 10 sequences.

References

Acqualagna, L., & Blankertz, B. (2013). Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clinical Neurophysiology, 124(5), 901-908. https://doi.org/10.1016/j.clinph.2012.12.050 Notes .. versionadded:: 1.2.0 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=12, range 29–29 yr, mean 29.0 yr)

25

Other · 12

Channel counts (ch)

6163

Sampling frequencies: 200.0 Hz (n=24 recordings)

Total recording duration: 16 h 9 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 (22), 61 (2) ch · EEG · 200 Hz · 12 subjects, 24 recordings

Live trace viewer — sub-12 · ses-0 · task-p300 · run-0

Showing one representative recording out of 12 subjects and 24 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 · 63 sensors — 63 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.NM000194(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Acqualagna2015

Canonical—

Importable asNM000194 · Acqualagna2015

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

class eegdash.dataset.NM000194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2015-010 RSVP P300 dataset

Study:: nm000194 (NeMAR)
Author (year):: Acqualagna2015
Canonical:: —

Also importable as: NM000194, Acqualagna2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 24; 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/nm000194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000194

Examples

>>> from eegdash.dataset import NM000194
>>> dataset = NM000194(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 — NM000194.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 nm000194 to reproduce the tutorial on this dataset.

Citation

Laura Acqualagna, Benjamin Blankertz (2013). BNCI 2015-010 RSVP P300 dataset.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000198EEG · 13 subj NM000249EEG · 13 subj NM000212EEG · 16 subj NM000188EEG · 10 subj NM000259EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000194`
Title	BNCI 2015-010 RSVP P300 dataset
Author (year)	`Acqualagna2015`
Canonical	—
Importable as	`NM000194`, `Acqualagna2015`
Year	2013
Authors	Laura Acqualagna, Benjamin Blankertz
License	CC-BY-NC-ND-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000194: eeg dataset, 12 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#