NM000169: eeg dataset, 8 subjects

BNCI 2014-008 P300 dataset (ALS patients)

Access recordings and metadata through EEGDash.

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).

Modality: eeg Subjects: 8 Recordings: 8 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset

BNCI 2014-008 P300 dataset (ALS patients)

BNCI 2014-008 P300 dataset (ALS patients).

Dataset Overview

• Code: BNCI2014-008

• Paradigm: p300

• DOI: 10.3389/fnhum.2013.00732

View full README

BNCI 2014-008 P300 dataset (ALS patients)

BNCI 2014-008 P300 dataset (ALS patients).

Dataset Overview

• Code: BNCI2014-008

• Paradigm: p300

• DOI: 10.3389/fnhum.2013.00732

• Subjects: 8

• Sessions per subject: 1

• Events: Target=2, NonTarget=1

• Trial interval: [0, 1.0] s

• File format: Unknown

• Data preprocessed: True

Acquisition

• Sampling rate: 256.0 Hz

• Number of channels: 8

• Channel types: eeg=8

• Channel names: Fz, Cz, Pz, Oz, P3, P4, PO7, PO8

• Montage: 10-10

• Hardware: g.MOBILAB

• Software: BCI2000

• Reference: right earlobe

• Ground: left mastoid

• Sensor type: active electrodes

• Line frequency: 50.0 Hz

• Online filters: 0.1-10 Hz bandpass, 50 Hz notch

• Electrode type: g.Ladybird

• Electrode material: Ag/AgCl

Participants

• Number of subjects: 8

• Health status: ALS patients

• Clinical population: amyotrophic lateral sclerosis

• Age: mean=58.0, std=12.0, min=40, max=72

• Gender distribution: M=5, F=3

• BCI experience: naive

• Species: human

Experimental Protocol

• Paradigm: p300

• Number of classes: 2

• Class labels: Target, NonTarget

• Study design: P300 speller with 6x6 matrix for copy-spelling task in ALS patients

• Feedback type: visual

• Stimulus type: row-column intensification

• Stimulus modalities: visual

• Primary modality: visual

• Synchronicity: synchronous

• Mode: online

• Training/test split: True

• Instructions: Copy spell seven predefined words of five characters each by focusing attention on desired letters

HED Event Annotations

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

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) https://github.com/NeuroTechX/moabb

Dataset Information

Dataset ID	NM000169
Title	BNCI 2014-008 P300 dataset (ALS patients)
Author (year)	Riccio2014
Canonical	BNCI2014008
Importable as	NM000169, Riccio2014, BNCI2014008
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	Unknown
Source links	OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details

Subjects & recordings

• Subjects: 8

• Recordings: 8

• Tasks: 1

Channels & sampling rate

• Channels: 8

• Sampling rate (Hz): 256.0

• Duration (hours): 3.018255208333333

Tags

• Pathology: Other

• Modality: Visual

• Type: Attention

Files & format

• Size on disk: 75.9 MB

• File count: 8

• Format: BIDS

License & citation

• License: CC-BY-NC-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000169

• NeMAR: nm000169

API Reference

Use the NM000169 class to access this dataset programmatically.

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

Bases: EEGDashDataset

BNCI 2014-008 P300 dataset (ALS patients)

Study:

nm000169 (NeMAR)

Author (year):

Riccio2014

Canonical:

BNCI2014008

Also importable as: NM000169, Riccio2014, BNCI2014008.

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

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)

save(path, overwrite=False)

Save the dataset to disk.

Parameters:

• path (str or Path) – Destination file path.

• overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971