moabb.datasets.MartinezCagigal2023Checker#

class moabb.datasets.MartinezCagigal2023Checker(conditions=('c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8'), subjects=None, sessions=None, **kwargs)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

MartinezCagigal2023Checker

c-VEP, 2 classes (0.0 vs 1.0)

AuthorsÁlvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero

🇪🇸 University of Valladolid, ES·2023·victor.martinez@gib.tel.uva.es
c-VEP Code: MartinezCagigal2023Checkercvep 16 subjects 8 sessions 16 ch 256 Hz 2 classes 1.0 s trials CC BY-NC-SA 4.0

Class Labels: 0.0, 1.0

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Overview

Checkerboard m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2025) and Fernández-Rodríguez et al. (2023).

Dataset Description

This dataset, accessible at, was originally recorded for study, which evaluated 8 different stimuli in a c-VEP circular shifting paradigm using binary m-sequences. The conditions were tested in a 9-command speller. The stimulus was composed of a black-background checkerboard (BB-CB) pattern, i.e. event 1 was encoded with a checkerboard pattern and event 0 with a white flash. The stimuli were encoded using circularly shifting versions of a 63-bit binary m-sequence. The different conditions evaluated different spatial frequency variations of the BB-CB pattern (i.e., the number of squares inside the checkerboard pattern).

The evaluated conditions were:

  • - c1: C001 (0 c/º, 1x1 squares).
  • c2: C002 (0.15 c/º, 2x2 squares).
  • c3: C004 (0.3 c/º, 4x4 squares).
  • c4: C008 (0.6 c/º, 8x8 squares).
  • c5: C016 (1.2 c/º, 16x16 squares).
  • c6: C032 (2.4 c/º, 32x32 squares).
  • c7: C064 (4.79 c/º, 64x64 squares).
  • c8: C128 (9.58 c/º, 128x128 squares).

The dataset includes recordings from 16 healthy subjects performing a copy-spelling task under each condition. The evaluation was conducted in a single session, during which each participant completed:

1. A calibration phase consisting of 30 trials using the original m-sequence (divided into two recordings of 15 trials each), and 2. An online copy-spelling task of 18 trials (in one run).

Each trial consisted of 8 cycles (i.e., repetitions of the same code). Additionally, participants completed questionnaires to assess satisfaction and perceived eyestrain for each m-sequence condition. Questionnaire results are available in

The encoding was displayed at a 120 Hz refresh rate. EEG signals were recorded using a g.USBamp amplifier (g.Tec, Guger Technologies, Austria) with 16 active electrodes and a sampling rate of 256 Hz. Electrodes were placed at: Oz, F3, Fz, F4, I1, I2, C3, Cz, C4, CPz, P3, Pz, P4, PO7, POz, PO8, grounded at AFz and referenced to the earlobe.

The experimental paradigm was executed using the MEDUSA© software

:param conditions: Which conditions to load. Default is all conditions: ("c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8"). Each condition corresponds to a different spatial frequency of the checkerboard pattern. :type conditions: tuple of str, optional

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/MartinezCagigal2023Checker.svg

1s task window per trial · 2-class c-vep paradigm · 3 runs/session across 8 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Label
2
Sensory-event
2
Visual-presentation
2
0.0
Sensory-eventExperimental-stimulusVisual-presentationLabel
1.0
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 0.0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 1.0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels16
EEG16
Montage10-05
Sampling256 Hz
Notch / line50 Hz

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

Checkerboard m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2025) and Fernández-Rodríguez et al. (2023).

Dataset Description

This dataset, accessible at [1], was originally recorded for study [2], which evaluated 8 different stimuli in a c-VEP circular shifting paradigm using binary m-sequences. The conditions were tested in a 9-command speller. The stimulus was composed of a black-background checkerboard (BB-CB) pattern, i.e. event 1 was encoded with a checkerboard pattern and event 0 with a white flash. The stimuli were encoded using circularly shifting versions of a 63-bit binary m-sequence. The different conditions evaluated different spatial frequency variations of the BB-CB pattern (i.e., the number of squares inside the checkerboard pattern).

The evaluated conditions were:

  • c1: C001 (0 c/º, 1x1 squares).

  • c2: C002 (0.15 c/º, 2x2 squares).

  • c3: C004 (0.3 c/º, 4x4 squares).

  • c4: C008 (0.6 c/º, 8x8 squares).

  • c5: C016 (1.2 c/º, 16x16 squares).

  • c6: C032 (2.4 c/º, 32x32 squares).

  • c7: C064 (4.79 c/º, 64x64 squares).

  • c8: C128 (9.58 c/º, 128x128 squares).

The dataset includes recordings from 16 healthy subjects performing a copy-spelling task under each condition. The evaluation was conducted in a single session, during which each participant completed:

  1. A calibration phase consisting of 30 trials using the original m-sequence (divided into two recordings of 15 trials each), and

  2. An online copy-spelling task of 18 trials (in one run).

Each trial consisted of 8 cycles (i.e., repetitions of the same code). Additionally, participants completed questionnaires to assess satisfaction and perceived eyestrain for each m-sequence condition. Questionnaire results are available in [1].

The encoding was displayed at a 120 Hz refresh rate. EEG signals were recorded using a g.USBamp amplifier (g.Tec, Guger Technologies, Austria) with 16 active electrodes and a sampling rate of 256 Hz. Electrodes were placed at: Oz, F3, Fz, F4, I1, I2, C3, Cz, C4, CPz, P3, Pz, P4, PO7, POz, PO8, grounded at AFz and referenced to the earlobe.

The experimental paradigm was executed using the MEDUSA© software [3].

param conditions:

Which conditions to load. Default is all conditions: (“c1”, “c2”, “c3”, “c4”, “c5”, “c6”, “c7”, “c8”). Each condition corresponds to a different spatial frequency of the checkerboard pattern.

type conditions:

tuple of str, optional

References

[1] (1,2)

Martínez Cagigal, V. (2025). Dataset: Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. https://doi.org/10.71569/7c67-v596

[2]

Fernández-Rodríguez, Á., Martínez-Cagigal, V., Santamaría-Vázquez, E., Ron-Angevin, R., & Hornero, R. (2023). Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. Frontiers in Human Neuroscience, 17, 1288438. https://doi.org/10.3389/fnhum.2023.1288438

[3]

Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S., Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate brain–computer interface and cognitive neuroscience research. Computer Methods and Programs in Biomedicine, 230, 107357. https://doi.org/10.1016/j.cmpb.2023.107357

from moabb.datasets import MartinezCagigal2023Checker
dataset = MartinezCagigal2023Checker()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

16

#Chan

16

#Trials / class

2-30

Trials length

4.2 s

Freq

256 Hz

#Sessions

8

#Trial classes

16

#Epochs classes

2

#Epochs / class

11904/12288

Codes

m-sequence

Presentation rate

120 Hz

Participants

  • Population: healthy

Equipment

  • Montage: standard_1005

Data Access

Experimental Protocol

  • Paradigm: cvep

Notes

Although the dataset was recorded in a single session, each condition is stored as a separate session to match the MOABB structure. Within each session, three runs are available (two for training, one for testing).

Added in version 1.2.0.

__init__(conditions=('c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8'), subjects=None, sessions=None, **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])

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]#

Return the data paths of a single subject.

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