moabb.datasets.Lee2019_SSVEP#

class moabb.datasets.Lee2019_SSVEP(train_run=True, test_run=None, resting_state=False, sessions=None, subjects=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: Lee2019

[source]

Dataset Snapshot

Lee2019_SSVEP

EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

SSVEP, 4 classes (12.0 vs 8.57 vs 6.67 vs 5.45)

AuthorsMin-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee

🇰🇷 Korea University, KR·2019·sw.lee@korea.ac.kr
SSVEP Code: Lee2019-SSVEP 54 subjects 2 sessions 62 ch 1000 Hz 4 classes 4.0 s trials GPL 3.0

Class Labels: 12.0, 8.57, 6.67, 5.45

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Overview

BMI/OpenBMI dataset for SSVEP.

Dataset from Lee et al 2019

Dataset Description

EEG signals were recorded with a sampling rate of 1,000 Hz and collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the experiment was a BrainAmp (Brain Products; Munich, Germany). The channels were nasion-referenced and grounded to electrode AFz. Additionally, an EMG electrode recorded from each flexor digitorum profundus muscle with the olecranon used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the EEG electrodes were maintained below 10 k during the entire experiment.

SSVEP paradigm Four target SSVEP stimuli were designed to flicker at 5.45, 6.67, 8.57, and 12 Hz and were presented in four positions (down, right, left, and up, respectively) on a monitor. The designed paradigm followed the conventional types of SSVEP-based BCI systems that require four-direction movements. Partici- pants were asked to fixate the center of a black screen and then to gaze in the direction where the target stimulus was high- lighted in a different color. Each SSVEP stimulus was presented for 4 s with an ISI of 6 s. Each target frequency was presented 25 times. Therefore, the corrected EEG data had 100 trials (4 classes x 25 trials) in the offline training phase and another 100 trials in the online test phase. Visual feedback was presented in the test phase; the estimated target frequency was highlighted for 1 s with a red border at the end of each trial.

:param train_run: if True, return runs corresponding to the training/offline phase (see paper). :type train_run: bool (default True) :param test_run: if True, return runs corresponding to the test/online phase (see paper). Beware that test_run for MI and SSVEP do not have labels associated with trials: these runs could not be used in classification tasks. :type test_run: bool (default: False for MI and SSVEP paradigms, True for ERP) :param resting_state: if True, return runs corresponding to the resting phases before and after recordings (see paper). :type resting_state: bool (default False) :param sessions: the list of the sessions to load (2 available). :type sessions: list of int (default [1,2])

Open in Overview tab →

Benchmark Context

WithinSession

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

Sample frame: 54 subjects × 2 sessions

  • SSVEP all classes 6 pipelinesMax 89.44 · Median 69.69 · Mean 61.06 · Std 29.84

Citation & Impact

Stimulus Protocol
../_images/Lee2019_SSVEP.svg

4s task window per trial · 4-class ssvep paradigm · 1 runs/session across 2 sessions

HED Event Tags
HED tags4/4 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
4
Label
4
Sensory-event
4
Visual-presentation
4
12.0
Sensory-eventExperimental-stimulusVisual-presentationLabel
8.57
Sensory-eventExperimental-stimulusVisual-presentationLabel
6.67
Sensory-eventExperimental-stimulusVisual-presentationLabel
5.45
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 12.0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 8.57
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 6.67
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 5.45
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels62
EEG62 (Ag/AgCl)
EMG4
Montage10-05
Sampling1000 Hz
Referencenasion
Notch / line60 Hz

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

BMI/OpenBMI dataset for SSVEP.

Dataset from Lee et al 2019 [1].

Dataset Description

EEG signals were recorded with a sampling rate of 1,000 Hz and collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the experiment was a BrainAmp (Brain Products; Munich, Germany). The channels were nasion-referenced and grounded to electrode AFz. Additionally, an EMG electrode recorded from each flexor digitorum profundus muscle with the olecranon used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the EEG electrodes were maintained below 10 k during the entire experiment.

SSVEP paradigm Four target SSVEP stimuli were designed to flicker at 5.45, 6.67, 8.57, and 12 Hz and were presented in four positions (down, right, left, and up, respectively) on a monitor. The designed paradigm followed the conventional types of SSVEP-based BCI systems that require four-direction movements. Partici- pants were asked to fixate the center of a black screen and then to gaze in the direction where the target stimulus was high- lighted in a different color. Each SSVEP stimulus was presented for 4 s with an ISI of 6 s. Each target frequency was presented 25 times. Therefore, the corrected EEG data had 100 trials (4 classes x 25 trials) in the offline training phase and another 100 trials in the online test phase. Visual feedback was presented in the test phase; the estimated target frequency was highlighted for 1 s with a red border at the end of each trial.

param train_run:

if True, return runs corresponding to the training/offline phase (see paper).

type train_run:

bool (default True)

param test_run:

if True, return runs corresponding to the test/online phase (see paper). Beware that test_run for MI and SSVEP do not have labels associated with trials: these runs could not be used in classification tasks.

type test_run:

bool (default: False for MI and SSVEP paradigms, True for ERP)

param resting_state:

if True, return runs corresponding to the resting phases before and after recordings (see paper).

type resting_state:

bool (default False)

param sessions:

the list of the sessions to load (2 available).

type sessions:

list of int (default [1,2])

References

[1]

Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002

from moabb.datasets import Lee2019_SSVEP
dataset = Lee2019_SSVEP()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

54

#Chan

62

#Classes

4

#Trials / class

50

Trials length

4 s

Freq

1000 Hz

#Sessions

2

Participants

  • Population: healthy

  • BCI experience: mixed

Equipment

  • Amplifier: BrainAmp

  • Electrodes: Ag/AgCl

  • Montage: standard_1005

  • Reference: nasion

Preprocessing

  • Data state: raw EEG available

  • Steps: downsampling

Data Access

  • DOI: 10.1093/gigascience/giz002

  • Repository: GigaDB

Experimental Protocol

  • Paradigm: ssvep

  • Task type: selective_attention

  • Feedback: visual

  • Stimulus: flickering_box

__init__(train_run=True, test_run=None, resting_state=False, sessions=None, subjects=None, *, return_all_modalities=False, **kwargs)[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