moabb.datasets.Kalunga2016#

class moabb.datasets.Kalunga2016(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Kalunga2016

Online SSVEP-based BCI using Riemannian geometry for assistive robotics with shared control scheme

SSVEP, 4 classes (13 vs 17 vs 21 vs rest)

AuthorsEmmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam

🇫🇷 Universite de Versailles Saint-Quentin, FR·2016
SSVEP Code: Kalunga2016 12 subjects 1 session 8 ch 256 Hz 4 classes 6.0 s trials CC BY 4.0

Class Labels: 13, 17, 21, rest

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Overview

SSVEP Exo dataset.

SSVEP dataset from E. Kalunga PhD in University of Versailles

The datasets contains recording from 12 male and female subjects aged between 20 and 28 years. Informed consent was obtained from all subjects, each one has signed a form attesting her or his consent. The subject sits in an electric wheelchair, his right upper limb is resting on the exoskeleton. The exoskeleton is functional but is not used during the recording of this experiment.

A panel of size 20x30 cm is attached on the left side of the chair, with 3 groups of 4 LEDs blinking at different frequencies. Even if the panel is on the left side, the user could see it without moving its head. The subjects were asked to sit comfortably in the wheelchair and to follow the auditory instructions, they could move and blink freely.

A sequence of trials is proposed to the user. A trial begin by an audio cue indicating which LED to focus on, or to focus on a fixation point set at an equal distance from all LEDs for the reject class. A trial lasts 5 seconds and there is a 3 second pause between each trial. The evaluation is conducted during a session consisting of 32 trials, with 8 trials for each frequency (13Hz, 17Hz and 21Hz) and 8 trials for the reject class, i.e. when the subject is not focusing on any specific blinking LED.

There is between 2 and 5 sessions for each user, recorded on different days, by the same operators, on the same hardware and in the same conditions.

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 12 subjects × 1 sessions

  • SSVEP all classes 6 pipelinesMax 70.89 · Median 47.17 · Mean 47.27 · Std 25.18

Citation & Impact

Stimulus Protocol
../_images/Kalunga2016.svg

6s task window per trial · 4-class ssvep paradigm · 1 runs/session across 1 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
3
Label
3
Sensory-event
3
Visual-presentation
3
Experiment-structure
1
Rest
1
13
Sensory-eventExperimental-stimulusVisual-presentationLabel
17
Sensory-eventExperimental-stimulusVisual-presentationLabel
21
Sensory-eventExperimental-stimulusVisual-presentationLabel
rest
Experiment-structureRest

HED tree view

Tree · 13
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 17
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 21
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · rest
├─ Experiment-structure
└─ Rest
Channel Summary
Total channels8
EEG8 (EEG)
Montage10-05
Sampling256 Hz
Referenceright mastoid
Notch / line50 Hz

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

SSVEP Exo dataset.

SSVEP dataset from E. Kalunga PhD in University of Versailles [1].

The datasets contains recording from 12 male and female subjects aged between 20 and 28 years. Informed consent was obtained from all subjects, each one has signed a form attesting her or his consent. The subject sits in an electric wheelchair, his right upper limb is resting on the exoskeleton. The exoskeleton is functional but is not used during the recording of this experiment.

A panel of size 20x30 cm is attached on the left side of the chair, with 3 groups of 4 LEDs blinking at different frequencies. Even if the panel is on the left side, the user could see it without moving its head. The subjects were asked to sit comfortably in the wheelchair and to follow the auditory instructions, they could move and blink freely.

A sequence of trials is proposed to the user. A trial begin by an audio cue indicating which LED to focus on, or to focus on a fixation point set at an equal distance from all LEDs for the reject class. A trial lasts 5 seconds and there is a 3 second pause between each trial. The evaluation is conducted during a session consisting of 32 trials, with 8 trials for each frequency (13Hz, 17Hz and 21Hz) and 8 trials for the reject class, i.e. when the subject is not focusing on any specific blinking LED.

There is between 2 and 5 sessions for each user, recorded on different days, by the same operators, on the same hardware and in the same conditions.

References

[1]

Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. “Online SSVEP-based BCI using Riemannian Geometry”. Neurocomputing, 2016. arXiv report: https://arxiv.org/abs/1501.03227

from moabb.datasets import Kalunga2016
dataset = Kalunga2016()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

8

#Classes

4

#Trials / class

16

Trials length

2 s

Freq

256 Hz

#Sessions

1

Participants

  • Population: healthy

Equipment

  • Amplifier: g.tec MobiLab

  • Electrodes: EEG

  • Montage: standard_1005

  • Reference: right mastoid

Data Access

Experimental Protocol

  • Paradigm: ssvep

  • Feedback: none

  • Stimulus: flickering

Notes

Note

Kalunga2016 was previously named SSVEPExo. SSVEPExo will be removed in version 1.1.

The events notation 17Hz and 21Hz were swapped after an investigation conducted by ponpopon at Github.

The dataset includes recordings from 12 healthy subjects.

Added in version 1.2.0.

__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])

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

Examples using moabb.datasets.Kalunga2016#

Cross-Subject SSVEP

Cross-Subject SSVEP

Within Session SSVEP

Within Session SSVEP