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)

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) NeuroTechX/moabb

Dataset Information#

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	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

Electrode Layout#

Electrode layout — EEG · 8 sensors — 8 channels

Dataset Statistics#

Age distribution (n=8, range 40–75 yr)

4055607075

Sex distribution

3

5

Female Male Total: 8

Channel counts: 8 ch (n=8 recordings)

Sampling frequencies: 256.0 Hz (n=8 recordings)

Total recording duration: 3 h 1 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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)[source]#

Bases: EEGDashDataset

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

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)