moabb.datasets.MartinezCagigal2023Pary

class moabb.datasets.MartinezCagigal2023Pary(conditions=('2', '3', '5', '7', '11'), subjects=None, sessions=None, **kwargs)

Bases: BaseDataset

[source]

Dataset Snapshot

MartinezCagigal2023Pary

c-VEP, 11 classes

AuthorsVíctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, Roberto Hornero

🇪🇸 University of Valladolid, ES·2023·victor.martinez@gib.tel.uva.es

c-VEP Code: MartinezCagigal2023Parycvep 16 subjects 5 sessions 16 ch 256 Hz 11 classes 1.0 s trials CC BY-NC-SA 4.0

Class Labels: 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, ...

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Overview

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

Dataset Description

This dataset was originally recorded for study, which evaluated different non-binary encoding strategies. Specifically, five different conditions were tested in a 16-command speller. Each condition used a different p-ary m-sequence to encode the commands via circular shifting. One command was encoded using the original m-sequence, while the remaining commands were encoded using shifted versions of that sequence

A p-ary m-sequence means it contains p different events, which were encoded using different shades of gray. For example, in the binary case (p=2), events 0 and 1 were encoded using white and black flashes, respectively. For p=3, black, white, and mid-gray flashes were used

The evaluated conditions were:

• - Base 2: GF(2^6) m-sequence of 63 bits
• Base 3: GF(3^4) m-sequence of 80 bits
• Base 5: GF(5^3) m-sequence of 124 bits
• Base 7: GF(7^2) m-sequence of 48 bits
• Base 11: GF(11^2) m-sequence of 120 bits

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 six recordings of five trials each), and 2. An online copy-spelling task of 32 trials (divided into two recordings of 16 trials each).

Each trial consisted of 10 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: F3, Fz, F4, C3, Cz, C4, CPz, P3, Pz, P4, PO7, PO8, Oz, I1, and I2; 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: ("2", "3", "5", "7", "11"). Each condition corresponds to a different p-ary m-sequence base. :type conditions: tuple of str, optional

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Citation & Impact

• Paper DOI10.1016/j.eswa.2023.120815
• CitationsLoading…
• Public APICrossref | OpenAlex
• Data DOI10.71569/025s-eq10
• Page Views
  30d: 5 · all-time: 7

  #96 of 151 · Top 64% most viewed

  Updated: 2026-03-20 UTC

Stimulus Protocol

1s task window per trial · 11-class c-vep paradigm · 8 runs/session across 5 sessions

HED Event Tags

HED tags11/11 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus

11

Label

11

Sensory-event

11

Visual-presentation

11

0.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

1.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

2.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

3.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

4.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

5.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

6.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

7.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

8.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

9.0

Sensory-eventExperimental-stimulusVisual-presentationLabel

10.0

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

Tree · 2.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 3.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 4.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 5.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 6.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 7.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 8.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 9.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label

Tree · 10.0

├─ 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.

Overview

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

Dataset Description

This dataset was originally recorded for study [1], which evaluated different non-binary encoding strategies. Specifically, five different conditions were tested in a 16-command speller. Each condition used a different p-ary m-sequence to encode the commands via circular shifting. One command was encoded using the original m-sequence, while the remaining commands were encoded using shifted versions of that sequence [2].

A p-ary m-sequence means it contains p different events, which were encoded using different shades of gray. For example, in the binary case (p=2), events 0 and 1 were encoded using white and black flashes, respectively. For p=3, black, white, and mid-gray flashes were used [1].

The evaluated conditions were:

• Base 2: GF(2^6) m-sequence of 63 bits

• Base 3: GF(3^4) m-sequence of 80 bits

• Base 5: GF(5^3) m-sequence of 124 bits

• Base 7: GF(7^2) m-sequence of 48 bits

• Base 11: GF(11^2) m-sequence of 120 bits

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 six recordings of five trials each), and

2. An online copy-spelling task of 32 trials (divided into two recordings of 16 trials each).

Each trial consisted of 10 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 [3].

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: F3, Fz, F4, C3, Cz, C4, CPz, P3, Pz, P4, PO7, PO8, Oz, I1, and I2; grounded at AFz and referenced to the earlobe.

Note

Recordings of user “zdvm” for bases 2, 3, 5, and 7 had a sampling rate of 600 Hz. The rest of recordings have all a sampling rate of 256 Hz.

The experimental paradigm was executed using the MEDUSA© software [4].

param conditions:

Which conditions to load. Default is all conditions: (“2”, “3”, “5”, “7”, “11”). Each condition corresponds to a different p-ary m-sequence base.

type conditions:

tuple of str, optional

References

[1] (1,2)

Martínez-Cagigal, V., Santamaría-Vázquez, E., Pérez-Velasco, S., Marcos-Martínez, D., Moreno-Calderón, S., & Hornero, R. (2023). Non-binary m-sequences for more comfortable brain-computer interfaces based on c-VEPs. Expert Systems with Applications, 232, 120815. https://doi.org/10.1016/j.eswa.2023.120815

[2]

Martínez-Cagigal, V., Thielen, J., Santamaría-Vázquez, E., Pérez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): A literature review. Journal of Neural Engineering, 18(6), 061002. https://doi.org/10.1088/1741-2552/ac38cf

[3]

Martínez-Cagigal, V. (2025). Dataset: Non-binary m-sequences for more comfortable brain-computer interfaces based on c-VEPs. https://doi.org/10.35376/10324/70945

[4]

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

Code Examples

from moabb.datasets import MartinezCagigal2023Pary
dataset = MartinezCagigal2023Pary()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	16
#Chan	16
#Trials / class	2-30
Trials length	5.3/6.7/10.3/4.0/10.0 s
Freq	256 Hz
#Sessions	5
#Trial classes	16
#Epochs classes	2-11
#Epochs / class	6200-19220
Codes	p-ary m-sequence
Presentation rate	120 Hz

Participants

• Population: healthy

Equipment

• Montage: standard_1005

Data Access

• DOI: 10.71569/025s-eq10

• Data URL: https://doi.org/10.71569/025s-eq10

Experimental Protocol

• Paradigm: cvep

Notes

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, eight runs are available (six for training, two for testing).

Added in version 1.2.0.

__init__(conditions=('2', '3', '5', '7', '11'), subjects=None, sessions=None, **kwargs)

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)

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)

Return the data paths of a single subject.

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)

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

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)

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)

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

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