moabb.datasets.Dreyer2023#

class moabb.datasets.Dreyer2023(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: _Dreyer2023Base

[source]

Dataset Snapshot

Dreyer2023

A large EEG database with users' profile information for motor imagery brain-computer interface research. Contains electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI).

Motor Imagery, 2 classes (left_hand vs right_hand)

AuthorsPauline Dreyer, Aline Roc, Léa Pillette, Sébastien Rimbert, Fabien Lotte

🇫🇷 Centre Inria de l'université de Bordeaux, FR·2023·fabien.lotte@inria.fr
Motor Imagery Code: Dreyer2023 87 subjects 1 session 27 ch 512 Hz 2 classes 8.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand

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Overview

Class for Dreyer2023 dataset management. MI dataset.

Dataset description

"A large EEG database with users' profile information for motor imagery Brain-Computer Interface research"

:Data Collectors: Appriou Aurélien; Caselli Damien; Benaroch Camille; Yamamoto Sayu Maria; Roc Aline; Lotte Fabien; Dreyer Pauline; Pillette Léa :Data Manager: Dreyer Pauline :Project leader: Lotte Fabien :Project members: Rimbert Sébastien; Monseigne Thibaut

Dataset Dreyer2023 contains concatenated datasets Dreyer2023A, Dreyer2023B and Dreyer2023C.

Experiments were conducted by six experimenters. In addition, for each recording the following pieces of information are provided: subject's demographic, personality and cognitive profiles, the OpenViBE experimental instructions and codes, and experimenter's gender.

The experiment is designed for the investigation of the impact of the participant's and experimenter's gender on MI BCI performance

A recording contains open and closed eyes baseline recordings and 6 runs of the MI experiments. First 2 runs (acquisition runs) were used to train system and the following 4 runs (training runs) to train the participant. Each run contained 40 trials

Each trial was recorded as follows:

  • - t=0.00s cross displayed on screen
  • t=2.00s acoustic signal announced appearance of a red arrow
  • t=3.00s a red arrow appears (subject starts to perform task)
  • t=4.25s the red arrow disappears
  • t=4.25s the feedback on performance is given in the form of a blue bar with update frequency of 14 Hz
  • t=8.00s cross turns off (subject stops to perform task)

EEG signals:

- recorded with 27 electrodes, namely: Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3 (10-20 system), referenced to the left earlobe.

EOG signals:

- recorded with 3 electrodes, namely: EOG1, EOG2, EOG3 placed below, above and on the side of one eye.

EMG signals:

- recorded with 2 electrodes, namely: EMGg, EMGd placed 2.5cm below the skinfold on each wrist.

Demographic and biosocial information includes:

  • - gender, birth year, laterality
  • vision, vision assistance
  • familiarity to cognitive science or neurology, level of education
  • physical activity, meditation
  • attentional, neurological, psychiatrics symptoms

Personality and the cognitive profile:

  • - evaluated via 5th edition of the 16 Personality Factors (16PF5) test
  • and mental rotation test
  • index of learning style

Pre and post experiment questionnaires:

- evaluation of pre and post mood, mindfulness and motivational states

The online OpenViBE BCI classification performance:

- only performance measure used to give the feedback to the participants

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/Dreyer2023.svg

8s task window per trial · 2-class motor imagery paradigm · 6 runs/session across 1 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
2
Sensory-event
2
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action

HED tree view

Tree · left_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand
Tree · right_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand
Channel Summary
Total channels27
EEG27 (active electrodes)
EOG3
EMG2
Montage10-20
Sampling512 Hz
Referenceleft earlobe
Filternone (raw signals recorded without hardware filters)
Notch / line50 Hz

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

Class for Dreyer2023 dataset management. MI dataset.

Dataset description

“A large EEG database with users’ profile information for motor imagery Brain-Computer Interface research” [1] [2]

Data Collectors:

Appriou Aurélien; Caselli Damien; Benaroch Camille; Yamamoto Sayu Maria; Roc Aline; Lotte Fabien; Dreyer Pauline; Pillette Léa

Data Manager:

Dreyer Pauline

Project leader:

Lotte Fabien

Project members:

Rimbert Sébastien; Monseigne Thibaut

Dataset Dreyer2023 contains concatenated datasets Dreyer2023A, Dreyer2023B and Dreyer2023C.

Experiments were conducted by six experimenters. In addition, for each recording the following pieces of information are provided: subject’s demographic, personality and cognitive profiles, the OpenViBE experimental instructions and codes, and experimenter’s gender.

The experiment is designed for the investigation of the impact of the participant’s and experimenter’s gender on MI BCI performance [1].

A recording contains open and closed eyes baseline recordings and 6 runs of the MI experiments. First 2 runs (acquisition runs) were used to train system and the following 4 runs (training runs) to train the participant. Each run contained 40 trials [1].

Each trial was recorded as follows [1]:

  • t=0.00s cross displayed on screen

  • t=2.00s acoustic signal announced appearance of a red arrow

  • t=3.00s a red arrow appears (subject starts to perform task)

  • t=4.25s the red arrow disappears

  • t=4.25s the feedback on performance is given in the form of a blue bar with update frequency of 14 Hz

  • t=8.00s cross turns off (subject stops to perform task)

EEG signals [1]:

  • recorded with 27 electrodes, namely: Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3 (10-20 system), referenced to the left earlobe.

EOG signals [1]:

  • recorded with 3 electrodes, namely: EOG1, EOG2, EOG3 placed below, above and on the side of one eye.

EMG signals [1]:

  • recorded with 2 electrodes, namely: EMGg, EMGd placed 2.5cm below the skinfold on each wrist.

Demographic and biosocial information includes:

  • gender, birth year, laterality

  • vision, vision assistance

  • familiarity to cognitive science or neurology, level of education

  • physical activity, meditation

  • attentional, neurological, psychiatrics symptoms

Personality and the cognitive profile [1]:

  • evaluated via 5th edition of the 16 Personality Factors (16PF5) test

  • and mental rotation test

  • index of learning style

Pre and post experiment questionnaires [1]:

  • evaluation of pre and post mood, mindfulness and motivational states

The online OpenViBE BCI classification performance [1]:

  • only performance measure used to give the feedback to the participants

References

[1] (1,2,3,4,5,6,7,8,9,10)

Pillette, L., Roc, A., N’kaoua, B., & Lotte, F. (2021). Experimenters’ influence on mental-imagery based brain-computer interface user training. International Journal of Human-Computer Studies, 149, 102603.

[2]

Benaroch, C., Yamamoto, M. S., Roc, A., Dreyer, P., Jeunet, C., & Lotte, F. (2022). When should MI-BCI feature optimization include prior knowledge, and which one?. Brain-Computer Interfaces, 9(2), 115-128.

from moabb.datasets import Dreyer2023
dataset = Dreyer2023()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

87

#Chan

27

#Classes

2

#Trials / class

20

Trials length

5 s

Freq

512 Hz

#Sessions

1

#Runs

6

Total_trials

20880
__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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]#

Return the data BIDS paths of a single subject.

Parameters:

  • subject (int) – The subject number to fetch data for.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_(dataset) 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.

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

Returns:

A list containing the BIDSPath object for the subject’s data file.

Return type:

list

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()).

download_by_subject(subject, path=None)[source]#

Download and extract the dataset.

Parameters:

  • subject (int) – The subject number to download the dataset for.

  • path (str | None) – The path to the directory where the dataset should be downloaded. If None, the default directory is used.

Returns:

path – The dataset path.

Return type:

str

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

get_subject_info(path=None)[source]#

Return the demographic information of the subjects.

Returns:

A DataFrame containing the demographic information of the subjects.

Return type:

DataFrame

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