moabb.datasets.Simoes2020#

class moabb.datasets.Simoes2020(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Simoes2020

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsMarco Simoes, Davide Borra, Eduardo Santamaria-Vazquez, Mayra Bittencourt-Villalpando, Dominik Krzeminski, Aleksandar Miladinovic, Carlos Amaral, Bruno Direito, Miguel Castelo-Branco

🇵🇹 University of Coimbra, PT·2020
P300 / ERP Code: Simoes2020 15 subjects 7 sessions 8 ch 250 Hz 2 classes 1.2 s trials CC BY 4.0

Class Labels: Target, NonTarget

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Overview

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

Dataset from the paper

Dataset Description

Fifteen subjects with autism spectrum disorder (ASD) performed a P300-based BCI joint-attention training task across 7 sessions (105 total sessions). EEG was recorded at 250 Hz from 8 channels (C3, Cz, C4, CPz, P3, Pz, P4, POz) using a g.Nautilus wireless amplifier (g.tec).

The BCI used a virtual environment with 8 objects. One object per block was the target; the 8 objects flashed in rapid succession (10 runs per block in training, 3-10 in testing). Each flash produces a P300 response if it is the target object.

Data is pre-epoched: (8 channels x 350 samples x N trials). Each epoch spans -200 to +1200 ms relative to stimulus onset (1400 ms total at 250 Hz = 350 samples).

  • - Training: 1600 epochs per session (8 objects x 10 runs x 20 blocks)
  • Testing: 400 x K epochs per session (K = runs_per_block, 3-10)
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Citation & Impact

Stimulus Protocol
../_images/Simoes2020.svg

1.2s task window per trial · 2-class p300 / erp paradigm · 2 runs/session across 7 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Channel Summary
Total channels8
EEG8
Montagestandard_1020
Sampling250 Hz
Referenceright ear
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

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

Dataset from the paper [1].

Dataset Description

Fifteen subjects with autism spectrum disorder (ASD) performed a P300-based BCI joint-attention training task across 7 sessions (105 total sessions). EEG was recorded at 250 Hz from 8 channels (C3, Cz, C4, CPz, P3, Pz, P4, POz) using a g.Nautilus wireless amplifier (g.tec).

The BCI used a virtual environment with 8 objects. One object per block was the target; the 8 objects flashed in rapid succession (10 runs per block in training, 3-10 in testing). Each flash produces a P300 response if it is the target object.

Data is pre-epoched: (8 channels x 350 samples x N trials). Each epoch spans -200 to +1200 ms relative to stimulus onset (1400 ms total at 250 Hz = 350 samples).

  • Training: 1600 epochs per session (8 objects x 10 runs x 20 blocks)

  • Testing: 400 x K epochs per session (K = runs_per_block, 3-10)

References

[1]

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

from moabb.datasets import Simoes2020
dataset = Simoes2020()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

15

#Chan

8

#Trials / class

varies NT / T

Trials length

1 s

Freq

250 Hz

#Sessions

7

Participants

  • Population: patients

  • Clinical population: autism spectrum disorder (ASD)

  • Age: 22.17 (range: 16-38) years

Equipment

  • Amplifier: g.Nautilus (g.tec, wireless)

  • Montage: standard_1020

  • Reference: right ear

Data Access

Experimental Protocol

  • Paradigm: p300

  • Feedback: visual

  • Stimulus: object flash

__init__(subjects=None, sessions=None)[source]#

Initialize function for the BaseDataset.

property all_subjects#

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

  • path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

  • subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

  • verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Get path to local copy of a subject data.

Parameters:

  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type:

list of str

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • accept (bool) – Accept licence term to download the data, if any. Default: False

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str) → None | DataFrame[source]#

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pd.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

BaseDataset.get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray(). This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata: DatasetMetadata | None[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0