moabb.datasets.Chen2017SingleFlicker#

class moabb.datasets.Chen2017SingleFlicker(subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Chen2017SingleFlicker

SSVEP, 4 classes (north vs east vs west vs south)

AuthorsJingjing Chen, Dan Zhang, Andreas K. Engel, Qin Gong, Alexander Maye

🇩🇪 University Medical Center Hamburg-Eppendorf, DE·2017
SSVEP Code: Chen2017SingleFlicker 12 subjects 2 sessions 32 ch 2048 Hz 4 classes 3.5 s trials CC BY 4.0

Class Labels: north, east, west, south

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Overview

Single-flicker online SSVEP BCI dataset.

Dataset from

This dataset uses a spatially coded SSVEP paradigm where a single white square flickers at 15 Hz in the center of the screen. Four non-flickering target squares are placed at the cardinal directions (N, E, W, S). The user gazes at one target, producing a distinct spatial topography of the 15 Hz SSVEP response for each direction.

The dataset contains 32-channel EEG recorded from 12 healthy subjects (7 female, 5 male, mean age 23.5, range 19-32) using a BioSemi ActiveTwo system.

Two sessions are available per subject:

  • - Session "0" (training): Structured calibration data from .xdf files recorded at 2048 Hz. Each subject has 2 runs of 100 trials (50 per direction, 200 total), with ~3.5 s per trial. Requires pyxdf (install with pip install moabb[xdf]).
  • Session "1" (online): Adaptive BCI game data from .mat files recorded at 512 Hz. Variable-length trials from approximately 16 game rounds per subject.

Both sessions use the same BioSemi ActiveTwo cap with 32 EEG channels (A1-A32) and biosemi32 montage. The sampling rates differ between sessions (2048 Hz for training, 512 Hz for online).

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Citation & Impact

Stimulus Protocol
../_images/Chen2017SingleFlicker.svg

3.5s 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
north
Sensory-eventExperimental-stimulusVisual-presentationLabel
east
Sensory-eventExperimental-stimulusVisual-presentationLabel
west
Sensory-eventExperimental-stimulusVisual-presentationLabel
south
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · north
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · east
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · west
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · south
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels32
EEG32 (active)
Montagebiosemi32
Sampling2048 Hz
ReferenceCMS/DRL
Notch / line50 Hz

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

Single-flicker online SSVEP BCI dataset.

Dataset from [1].

This dataset uses a spatially coded SSVEP paradigm where a single white square flickers at 15 Hz in the center of the screen. Four non-flickering target squares are placed at the cardinal directions (N, E, W, S). The user gazes at one target, producing a distinct spatial topography of the 15 Hz SSVEP response for each direction.

The dataset contains 32-channel EEG recorded from 12 healthy subjects (7 female, 5 male, mean age 23.5, range 19-32) using a BioSemi ActiveTwo system.

Two sessions are available per subject:

  • Session “0” (training): Structured calibration data from .xdf files recorded at 2048 Hz. Each subject has 2 runs of 100 trials (50 per direction, 200 total), with ~3.5 s per trial. Requires pyxdf (install with pip install moabb[xdf]).

  • Session “1” (online): Adaptive BCI game data from .mat files recorded at 512 Hz. Variable-length trials from approximately 16 game rounds per subject.

Both sessions use the same BioSemi ActiveTwo cap with 32 EEG channels (A1-A32) and biosemi32 montage. The sampling rates differ between sessions (2048 Hz for training, 512 Hz for online).

Warning

This paradigm uses a SINGLE flicker frequency (15 Hz) with spatially-coded directions. Standard frequency-based SSVEP analysis (CCA, FBCCA) will NOT work. Use broadband spatial features or classification approaches instead.

References

[1]

J. Chen, D. Zhang, A. K. Engel, Q. Gong, and A. Maye, “Application of a single-flicker online SSVEP BCI for spatial navigation,” PLoS ONE, vol. 12, no. 5, e0178385, 2017. DOI: 10.1371/journal.pone.0178385

from moabb.datasets import Chen2017SingleFlicker
dataset = Chen2017SingleFlicker()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

32

#Classes

4

#Trials / class

varies

Trials length

3.5 s

Freq

512/2048 Hz

#Sessions

2

Participants

  • Population: healthy

  • Age: 23.5 (range: 19-32) years

Equipment

  • Amplifier: BioSemi ActiveTwo

  • Electrodes: active

  • Montage: biosemi32

  • Reference: CMS/DRL

Data Access

Experimental Protocol

  • Paradigm: ssvep

  • Task type: spatial navigation

  • Feedback: visual

  • Stimulus: single-flicker spatially coded

__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)[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]#

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