moabb.datasets.Cattan2019_PHMD#

class moabb.datasets.Cattan2019_PHMD(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Cattan2019_PHMD

This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display

Resting State, 2 classes (off vs on)

AuthorsG. Cattan, P. L. C. Rodrigues, M. Congedo

🇫🇷 GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP, FR·2019
Resting State Code: Cattan2019-PHMD 12 subjects 1 session 16 ch 512 Hz 2 classes 60.0 s trials CC BY 4.0

Class Labels: off, on

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Overview

Passive Head Mounted Display with Music Listening dataset

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2617084 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (Cattan and al, 2018). The ID of this dataset is PHMDML.EEG.2017-GIPSA.

full description of the experiment https://hal.archives-ouvertes.fr/hal-02085118

Link to the data https://doi.org/10.5281/zenodo.2617084

Authors Principal Investigator: Eng. Grégoire Cattan Technical Supervisors: Eng. Pedro L. C. Rodrigues Scientific Supervisor: Dr. Marco Congedo

ID of the dataset PHMDML.EEG.2017-GIPSA

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Citation & Impact

Stimulus Protocol
../_images/Cattan2019_PHMD.svg

60s task window per trial · 2-class resting state paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experiment-structure
2
Rest
2
off
Experiment-structureRest
on
Experiment-structureRest

HED tree view

Tree · off
├─ Experiment-structure
└─ Rest
Tree · on
├─ Experiment-structure
└─ Rest
Channel Summary
Total channels16
EEG16 (wet)
Montagestandard_1020
Sampling512 Hz
Referenceright earlobe
Filterno digital filter
Notch / line50 Hz

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

Passive Head Mounted Display with Music Listening dataset [1].

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2617084 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (Cattan and al, 2018). The ID of this dataset is PHMDML.EEG.2017-GIPSA.

full description of the experiment https://hal.archives-ouvertes.fr/hal-02085118

Link to the data https://doi.org/10.5281/zenodo.2617084

Authors Principal Investigator: Eng. Grégoire Cattan Technical Supervisors: Eng. Pedro L. C. Rodrigues Scientific Supervisor: Dr. Marco Congedo

ID of the dataset PHMDML.EEG.2017-GIPSA

References

[1]

G. Cattan, P. L. Coelho Rodrigues, and M. Congedo, ‘Passive Head-Mounted Display Music-Listening EEG dataset’, Gipsa-Lab ; IHMTEK, Research Report 2, Mar. 2019. doi: 10.5281/zenodo.2617084.

from moabb.datasets import Cattan2019_PHMD
dataset = Cattan2019_PHMD()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

16

#Classes

2

Trials length

60 s

Freq

512 Hz

#Sessions

1

#Blocks / class

5

Participants

  • Population: healthy

  • Age: 26.25 years

Equipment

  • Amplifier: g.USBamp

  • Electrodes: wet

  • Montage: standard_1020

  • Reference: right earlobe

Preprocessing

  • Data state: raw, unfiltered

  • Notes: Data were acquired with no digital filter. No Faraday cage used to mimic real-world usage.

Data Access

Experimental Protocol

  • Paradigm: rstate

  • Feedback: none

  • Stimulus: visual fixation marker

Notes

Added in version 1.0.0.

__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, generate_figures=False)[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.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

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])

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

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)[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 | pandas.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

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 (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.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[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

Examples using moabb.datasets.Cattan2019_PHMD#

Spectral analysis of the trials

Spectral analysis of the trials