moabb.datasets.compound_dataset.Cattan2019_VR_Il#

class moabb.datasets.compound_dataset.Cattan2019_VR_Il[source]#

Bases: _base_bi_il

[source]

Dataset Snapshot

Cattan2019_VR_Il

P300 / ERP, 2 classes (Target vs NonTarget)

P300 / ERP Code: Cattan2019-VR-Il 4 subjects 1 session 2 classes 1.0 s trials

Class Labels: Target, NonTarget

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Overview

A selection of subject from Cattan2019_VR with AUC < 0.7 with pipeline: ERPCovariances(estimator="lwf"), MDM(metric="riemann")

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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

Overview

A selection of subject from Cattan2019_VR with AUC < 0.7 with pipeline: ERPCovariances(estimator=”lwf”), MDM(metric=”riemann”)

Code Examples

from moabb.datasets import Cattan2019_VR_Il
dataset = Cattan2019_VR_Il()
data = dataset.get_data(subjects=[1])
print(data[1])

Notes

Note

Cattan2019_VR_Il was previously named VirtualReality_il. VirtualReality_il will be removed in version 1.1.

__init__()[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

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