NM000210: eeg dataset, 15 subjects#

BCIAUT-P300 dataset for autism from Simoes et al 2020

Access recordings and metadata through EEGDash.

Citation: Marco Simoes, Davide Borra, Eduardo Santamaria-Vazquez, Mayra Bittencourt-Villalpando, Dominik Krzeminski, Aleksandar Miladinovic, Carlos Amaral, Bruno Direito, Miguel Castelo-Branco (2020). BCIAUT-P300 dataset for autism from Simoes et al 2020.

Modality: eeg Subjects: 15 Recordings: 210 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000210

dataset = NM000210(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 = NM000210(cache_dir="./data", subject="01")

Advanced query

dataset = NM000210(
    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{nm000210,
  title = {BCIAUT-P300 dataset for autism from Simoes et al 2020},
  author = {Marco Simoes and Davide Borra and Eduardo Santamaria-Vazquez and Mayra Bittencourt-Villalpando and Dominik Krzeminski and Aleksandar Miladinovic and Carlos Amaral and Bruno Direito and Miguel Castelo-Branco},
}

About This Dataset#

BCIAUT-P300 dataset for autism from Simoes et al 2020

BCIAUT-P300 dataset for autism from Simoes et al 2020.

Dataset Overview

Code: Simoes2020
Paradigm: p300
DOI: 10.3389/fnins.2020.568104

View full README

BCIAUT-P300 dataset for autism from Simoes et al 2020

BCIAUT-P300 dataset for autism from Simoes et al 2020.

Dataset Overview

Code: Simoes2020
Paradigm: p300
DOI: 10.3389/fnins.2020.568104
Subjects: 15
Sessions per subject: 7
Events: Target=2, NonTarget=1
Trial interval: [0, 1.2] s
Runs per session: 2
File format: MATLAB (epoched)
Data preprocessed: True

Acquisition

Sampling rate: 250.0 Hz
Number of channels: 8
Channel types: eeg=8
Channel names: C3, Cz, C4, CPz, P3, Pz, P4, POz
Montage: standard_1020
Hardware: g.Nautilus (g.tec, wireless)
Reference: right ear
Ground: AFz
Line frequency: 50.0 Hz

Participants

Number of subjects: 15
Health status: patients
Clinical population: autism spectrum disorder (ASD)
Age: mean=22.17, std=5.5, min=16, max=38
Gender distribution: male=15
Species: human

Experimental Protocol

Paradigm: p300
Number of classes: 2
Class labels: Target, NonTarget
Trial duration: 1.2 s
Study design: P300 BCI joint-attention training in virtual environment; 8 flashing objects; 15 ASD subjects across 7 sessions (clinical trial NCT02445625)
Feedback type: visual
Stimulus type: object flash
Stimulus modalities: visual
Primary modality: visual
Mode: online

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

Data Structure

**Trials**: 1600 train + 400*K test per session (K=3-10)
Trials context: per_session

Signal Processing

Classifiers: EEGNet, LDA, SVM, MLP
Feature extraction: temporal_features, deep_learning
Frequency bands: bandpass=[2.0, 30.0] Hz

Cross-Validation

Method: calibration_vs_online
Evaluation type: within_subject, cross_session, cross_subject

BCI Application

Applications: joint_attention_training
Environment: clinical
Online feedback: True

Tags

Pathology: Autism
Modality: ERP
Type: P300

Documentation

DOI: 10.3389/fnins.2020.568104
License: CC-BY-4.0
Investigators: Marco Simoes, Davide Borra, Eduardo Santamaria-Vazquez, Mayra Bittencourt-Villalpando, Dominik Krzeminski, Aleksandar Miladinovic, Carlos Amaral, Bruno Direito, Miguel Castelo-Branco
Institution: University of Coimbra
Country: PT
Data URL: https://zenodo.org/records/19005186
Publication year: 2020

References

Simoes, M., Borra, D., Santamaria-Vazquez, E., et al. (2020). BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer- Interfaces. Frontiers in Neuroscience, 14, 568104. https://doi.org/10.3389/fnins.2020.568104 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	`NM000210`
Title	BCIAUT-P300 dataset for autism from Simoes et al 2020
Author (year)	`Simoes2020`
Canonical	—
Importable as	`NM000210`, `Simoes2020`
Year	2020
Authors	Marco Simoes, Davide Borra, Eduardo Santamaria-Vazquez, Mayra Bittencourt-Villalpando, Dominik Krzeminski, Aleksandar Miladinovic, Carlos Amaral, Bruno Direito, Miguel Castelo-Branco
License	CC-BY-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: 15
Recordings: 210
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 250.0
Duration (hours): 187.43532222222225

Electrode Layout#

Electrode layout — EEG · 8 sensors — 8 channels

Dataset Statistics#

Age distribution (n=15, range 22–22 yr)

20

Sex distribution

15

Male Total: 15

Channel counts: 8 ch (n=210 recordings)

Sampling frequencies: 250.0 Hz (n=210 recordings)

Total recording duration: 187 h

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 NM000210 class to access this dataset programmatically.

class eegdash.dataset.NM000210(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BCIAUT-P300 dataset for autism from Simoes et al 2020

Study:: nm000210 (NeMAR)
Author (year):: Simoes2020
Canonical:: —

Also importable as: NM000210, Simoes2020.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Development. Subjects: 15; recordings: 210; 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/nm000210 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000210

Examples

>>> from eegdash.dataset import NM000210
>>> dataset = NM000210(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)