EEGdash›NeMAR›NM000188
Iss. 188 · 10 subjects · 30 recordings · CC-BY-NC-ND-4.0
Dataset Brief · BNCI 2014-009 P300 dataset

NM000188: eeg dataset, 10 subjects#

BNCI 2014-009 P300 dataset

Citation: P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti (2013). BNCI 2014-009 P300 dataset. 10.82901/nemar.nm000188

10-participant EEG dataset — BNCI 2014-009 P300 dataset.

Data & curation P Aricò · F Aloise · F Schettini · S Salinari · D Mattia · F Cincotti
Year 2013 · Distributed via NeMAR
EEG · 16 ch256 HzBIDS 1.9.0Task · p3003 sessionsHealthyVisualAttention
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000188

dataset = NM000188(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000188(cache_dir="./data", subject="01")

Advanced query

dataset = NM000188(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000188,
  title = {BNCI 2014-009 P300 dataset},
  author = {P Aricò and F Aloise and F Schettini and S Salinari and D Mattia and F Cincotti},
  doi = {10.82901/nemar.nm000188},
  url = {https://doi.org/10.82901/nemar.nm000188},
}
§ 02Study · The README

About This Dataset#

BNCI 2014-009 P300 dataset.

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

DOI

BNCI 2014-009 P300 dataset

Target
View full README

DOI

BNCI 2014-009 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: 36

  • Number of repetitions: 8

  • Inter-stimulus interval: 125.0 ms

  • Stimulus onset asynchrony: 250.0 ms

Data Structure

  • Trials: 18

  • Blocks per session: 3

  • Trials context: 6 trials × 3 runs per session

Preprocessing

  • Data state: preprocessed

  • Preprocessing applied: True

  • Steps: bandpass filtering

  • Highpass filter: 0.1 Hz

  • Lowpass filter: 20.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 20.0}

  • Filter type: Butterworth

  • Filter order: 8

  • Re-reference: linked earlobes

  • Epoch window: [0.0, 0.8]

  • Notes: EEG acquired using g.USBamp amplifier (g.Tec, Austria), digitized at 256 Hz

Signal Processing

  • Classifiers: LDA, SWLDA

  • Feature extraction: Wavelet, Time-Frequency, CWT

  • Frequency bands: analyzed=[1.0, 20.0] Hz

Cross-Validation

  • Method: cross-validation

  • Folds: 3

  • Evaluation type: within_session

Performance (Original Study)

  • P300 Latency Jitter Correlation: negative correlation with accuracy

BCI Application

  • Applications: communication, spelling

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: P300, ERP

Documentation

  • Description: Complete record of P300 evoked potentials recorded with BCI2000 using two different paradigms: P300 Speller (overt attention) and GeoSpell (covert attention). 10 healthy subjects focused on one out of 36 different characters.

  • DOI: 10.1088/1741-2560/11/3/035008

  • Associated paper DOI: 10.3389/fnhum.2013.00732

  • License: CC-BY-NC-ND-4.0

  • Investigators: P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti

  • Senior author: F Cincotti

  • Contact: p.arico@hsantalucia.it

  • Institution: Fondazione Santa Lucia IRCCS

  • Department: Neuroelectrical Imaging and BCI Lab

  • Address: Rome, Italy

  • Country: Italy

  • Repository: BNCI Horizon

  • Publication year: 2014

  • Ethics approval: Approved by local Ethics Committee

  • Keywords: P300 latency jitter, brain-computer interface, covert attention, wavelet analysis, single epoch

Abstract

This dataset represents a complete record of P300 evoked potentials recorded with BCI2000 using two different paradigms: a paradigm based on the P300 Speller originally described by Farwell and Donchin in overt attention condition and a paradigm based on the GeoSpell interface used in covert attention condition. In these sessions, 10 healthy subjects focused on one out of 36 different characters. The objective was to predict the correct character in each of the provided character selection epochs.

Methodology

Ten healthy subjects (10 female, mean age = 26.8 ± 5.6) with previous experience with P300-based BCIs attended 4 recording sessions. Scalp EEG potentials were measured using 16 Ag/AgCl electrodes arranged on an elastic cap per the 10-10 standard. Each electrode was referenced to the linked earlobes and grounded to the right mastoid. The EEG was acquired using a g.USBamp amplifier (g.Tec, Austria), digitized at 256 Hz, high pass- and low pass-filtered with cutoff frequencies of 0.1 Hz and 20 Hz, respectively. The electrode impedance did not exceed 10 kΩ. Visual stimulation, acquisition and online classification were performed with BCI2000. Each subject attended 4 recording sessions. During each session, the subject performed three runs with each of the stimulation interfaces. Each trial consisted of eight stimulation sequences, and thus, 16 intensifications of the target character. Each stimulus was intensified for 125 ms, with an inter stimulus interval (ISI) of 125 ms, yielding a 250 ms lag between the appearance of two stimuli (SOA). Pseudorandom stimulation sequences were assembled so that each target intensification would not occur within 500 ms after the previous one to avoid the attentional blink phenomenon.

References

Riccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O., & Mattia, D. (2013). Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Frontiers in human neuroscience, 7, 732. https://doi.org/10.3389/fnhum.2013.00732 Notes .. note:: BNCI2014_009 was previously named BNCI2014009. BNCI2014009 will be removed in version 1.1. .. versionadded:: 0.4.0 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=10, range 22–40 yr, mean 26.8 yr)

20253540
Female · 10

Sex composition

10
subjects
Female
10

Channel counts: 16 ch (n=30 recordings)

Sampling frequencies: 256.0 Hz (n=30 recordings)

Total recording duration: 1 h 38 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 256 Hz · 10 subjects, 30 recordings
Live trace viewer — sub-6 · ses-2 · task-p300 · run-0

Showing one representative recording out of 10 subjects and 30 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.

NEMAR pipeline statistics — NM000188
HED event descriptors word cloud HED event descriptors word cloud — NM000188
§ 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…
Full dataset metadata table

Dataset ID

NM000188

Title

BNCI 2014-009 P300 dataset

Author (year)

Arico2014

Canonical

—

Importable as

NM000188, Arico2014

Year

2013

Authors

P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti

License

CC-BY-NC-ND-4.0

Citation / DOI

10.82901/nemar.nm000188

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000188,
  title = {BNCI 2014-009 P300 dataset},
  author = {P Aricò and F Aloise and F Schettini and S Salinari and D Mattia and F Cincotti},
  doi = {10.82901/nemar.nm000188},
  url = {https://doi.org/10.82901/nemar.nm000188},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000188(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Arico2014
Canonical—
Importable asNM000188 · Arico2014
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000188(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2014-009 P300 dataset

Study:

nm000188 (NeMAR)

Author (year):

Arico2014

Canonical:

—

Also importable as: NM000188, Arico2014.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 30; 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/nm000188 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000188 DOI: https://doi.org/10.82901/nemar.nm000188

Examples

>>> from eegdash.dataset import NM000188
>>> dataset = NM000188(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 — NM000188.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 nm000188 to reproduce the tutorial on this dataset.

Citation

P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, … (2013). BNCI 2014-009 P300 dataset. 10.82901/nemar.nm000188

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000188.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-NC-ND-4.0 · 10.82901/nemar.nm000188
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#