EEGdash›NeMAR›NM000169

Iss. 169 · 8 subjects · 8 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2014-008 P300 dataset (ALS patients)

NM000169: eeg dataset, 8 subjects#

Name: BNCI 2014-008 P300 dataset (ALS patients)
Published: 2013-01-01
License: CC-BY-NC-ND-4.0

BNCI 2014-008 P300 dataset (ALS patients)

Citation: Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti (2013). BNCI 2014-008 P300 dataset (ALS patients). 10.82901/nemar.nm000169

8-participant EEG dataset — BNCI 2014-008 P300 dataset (ALS patients).

Data & curation Angela Riccio · Luca Simione · Francesca Schettini · Alessia Pizzimenti · Maurizio Inghilleri · Marta Olivetti Belardinelli · …
Year 2013 · Distributed via NeMAR
Funding Italian Agency for Research on ALS-ARiSLA project 'Brindisys' · FARI project C26I12AJZZ at the Sapienza University of Rome

EEG · 8 ch256 HzBIDS 1.9.0Task · p300OtherVisualAttention

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 NM000169

dataset = NM000169(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 = NM000169(cache_dir="./data", subject="01")

Advanced query

dataset = NM000169(
    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{nm000169,
  title = {BNCI 2014-008 P300 dataset (ALS patients)},
  author = {Angela Riccio and Luca Simione and Francesca Schettini and Alessia Pizzimenti and Maurizio Inghilleri and Marta Olivetti Belardinelli and Donatella Mattia and Febo Cincotti},
  doi = {10.82901/nemar.nm000169},
  url = {https://doi.org/10.82901/nemar.nm000169},
}

§ 02Study · The README

About This Dataset#

BNCI 2014-008 P300 dataset (ALS patients).

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

DOI

BNCI 2014-008 P300 dataset (ALS patients)

Target

View full README

DOI

BNCI 2014-008 P300 dataset (ALS patients)

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: 10
Inter-stimulus interval: 125.0 ms
Stimulus onset asynchrony: 250.0 ms

Data Structure

Trials: 35
Blocks per session: 7
Trials context: per subject (7 words, 5 characters each)

Preprocessing

Data state: preprocessed
Preprocessing applied: True
Steps: bandpass filtering, notch filtering, artifact rejection, baseline correction
Highpass filter: 0.1 Hz
Lowpass filter: 10.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 10.0}
Notch filter: [50] Hz
Filter type: Butterworth
Filter order: 4
Artifact methods: amplitude threshold rejection
Re-reference: right earlobe
Epoch window: [0.0, 1.0]
Notes: Epochs with peak amplitude >70 μV or <-70 μV were rejected. Baseline correction based on 200 ms preceding each epoch.

Signal Processing

Classifiers: SWLDA
Feature extraction: temporal features, decimation

Cross-Validation

Method: 7-fold
Folds: 7
Evaluation type: within_subject

Performance (Original Study)

Accuracy: 97.5%
Binary Accuracy Offline: 87.4
P300 Amplitude Mean Uv: 3.3

BCI Application

Applications: communication
Environment: laboratory
Online feedback: True

Tags

Pathology: ALS
Modality: P300
Type: ERP

Documentation

DOI: 10.3389/fnhum.2013.00732
License: CC-BY-NC-ND-4.0
Investigators: Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti
Senior author: Febo Cincotti
Contact: a.riccio@hsantalucia.it
Institution: Fondazione Santa Lucia
Department: Neuroelectrical Imaging and BCI Laboratory
Address: Via Ardeatina, 306, 00179 Rome, Italy
Country: Italy
Repository: BNCI Horizon
Publication year: 2013
Funding: Italian Agency for Research on ALS-ARiSLA project ‘Brindisys’; FARI project C26I12AJZZ at the Sapienza University of Rome
Ethics approval: Fondazione Santa Lucia ethic committee
Keywords: brain computer interface, amyotrophic lateral sclerosis, P300, attention, working memory

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_008 was previously named BNCI2014008. BNCI2014008 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=8, range 40–75 yr, mean 58.1 yr)

4055607075

Female · 3Male · 5

Sex composition

Female
3
Male
5
F : M ratio
0.60 : 1

38% female · n = 8 subjects with reported sex.

Channel counts: 8 ch (n=8 recordings)

Sampling frequencies: 256.0 Hz (n=8 recordings)

Total recording duration: 3 h 1 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

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

Live trace viewer — sub-6 · ses-0 · task-p300 · run-0

Showing one representative recording out of 8 subjects and 8 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.NM000169(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Riccio2014

Canonical—

Importable asNM000169 · Riccio2014

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

class eegdash.dataset.NM000169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2014-008 P300 dataset (ALS patients)

Study:: nm000169 (NeMAR)
Author (year):: Riccio2014
Canonical:: —

Also importable as: NM000169, Riccio2014.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 8; 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/nm000169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000169 DOI: https://doi.org/10.82901/nemar.nm000169

Examples

>>> from eegdash.dataset import NM000169
>>> dataset = NM000169(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 — NM000169.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 nm000169 to reproduce the tutorial on this dataset.

Citation

Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, … (2013). BNCI 2014-008 P300 dataset (ALS patients). 10.82901/nemar.nm000169

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000169.

Related & sibling datasets

NM000187EEG · 8 subj NM000191EEG · 10 subj NM000248EEG · 11 subj NM000351EEG · 19 subj NM000197EEG · 21 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 · 10.82901/nemar.nm000169

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000169`
Title	BNCI 2014-008 P300 dataset (ALS patients)
Author (year)	`Riccio2014`
Canonical	—
Importable as	`NM000169`, `Riccio2014`
Year	2013
Authors	Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000169
Source links	OpenNeuro \| NeMAR \| Source URL

NM000169: eeg dataset, 8 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#