moabb.datasets.Thielen2021#

class moabb.datasets.Thielen2021(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Thielen2021

c-VEP, 2 classes (1.0 vs 0.0)

AuthorsJ Thielen, P Marsman, J Farquhar, P Desain

🇳🇱 Radboud University, NL·2021·jordy.thielen@donders.ru.nl
c-VEP Code: Thielen2021 30 subjects 1 session 8 ch 512 Hz 2 classes 31.5 s trials CC0 1.0

Class Labels: 1.0, 0.0

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Overview

c-VEP dataset from Thielen et al. (2021)

Dataset from the study on zero-training c-VEP

Dataset description

EEG recordings were acquired at a sampling rate of 512 Hz, employing 8 Ag/AgCl electrodes. The Biosemi ActiveTwo EEG amplifier was utilized during the experiment. The electrode array consisted of Fz, T7, O1, POz, Oz, Iz, O2, and T8, connected as EXG channels. This is a custom electrode montage as optimized in a previous study for c-VEP, see

During the experimental sessions, participants engaged in passive operation (i.e., without feedback) of a 4 x 5 visual speller brain-computer interface (BCI) comprising 20 distinct classes. Each cell of the symbol grid underwent luminance modulation at full contrast, accomplished through pseudo-random noise-codes derived from a collection of modulated Gold codes. These codes are binary, have a balanced distribution of ones and zeros, and adhere to a limited run-length pattern (maximum run-length of 2 bits). The codes were presented at a presentation rate of 60 Hz. As one cycle of these modulated Gold codes contains 126 bits, the duration of one cycle is 2.1 seconds.

For each of the five blocks, a trial started with a cueing phase, during which the target symbol was highlighted in a green hue for a duration of 1 second. Following this, participants maintained their gaze fixated on the target symbol while all symbols flashed in accordance with their respective pseudo-random noise-codes for a duration of 31.5 seconds (i.e., 15 code cycles). Each block encompassed 20 trials, presented in a randomized sequence, thereby ensuring that each symbol was attended to once within the span of a block.

Note, here, we only load the offline data of this study and ignore the online phase.

Open in Overview tab →

Citation & Impact

Stimulus Protocol
../_images/Thielen2021.svg

31.5s task window per trial · 2-class c-vep paradigm · 5 runs/session across 1 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
1.0
Sensory-eventExperimental-stimulusVisual-presentationLabel
0.0
Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 1.0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Tree · 0.0
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label
Channel Summary
Total channels8
EEG8 (sintered Ag/AgCl active electrodes)
Montagecustom
Sampling512 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.

c-VEP dataset from Thielen et al. (2021)

Dataset [1] from the study on zero-training c-VEP [2].

Dataset description

EEG recordings were acquired at a sampling rate of 512 Hz, employing 8 Ag/AgCl electrodes. The Biosemi ActiveTwo EEG amplifier was utilized during the experiment. The electrode array consisted of Fz, T7, O1, POz, Oz, Iz, O2, and T8, connected as EXG channels. This is a custom electrode montage as optimized in a previous study for c-VEP, see [3].

During the experimental sessions, participants engaged in passive operation (i.e., without feedback) of a 4 x 5 visual speller brain-computer interface (BCI) comprising 20 distinct classes. Each cell of the symbol grid underwent luminance modulation at full contrast, accomplished through pseudo-random noise-codes derived from a collection of modulated Gold codes. These codes are binary, have a balanced distribution of ones and zeros, and adhere to a limited run-length pattern (maximum run-length of 2 bits). The codes were presented at a presentation rate of 60 Hz. As one cycle of these modulated Gold codes contains 126 bits, the duration of one cycle is 2.1 seconds.

For each of the five blocks, a trial started with a cueing phase, during which the target symbol was highlighted in a green hue for a duration of 1 second. Following this, participants maintained their gaze fixated on the target symbol while all symbols flashed in accordance with their respective pseudo-random noise-codes for a duration of 31.5 seconds (i.e., 15 code cycles). Each block encompassed 20 trials, presented in a randomized sequence, thereby ensuring that each symbol was attended to once within the span of a block.

Note, here, we only load the offline data of this study and ignore the online phase.

References

[1]

Thielen, J. (Jordy), Pieter Marsman, Jason Farquhar, Desain, P.W.M. (Peter) (2023): From full calibration to zero training for a code-modulated visual evoked potentials brain computer interface. Version 3. Radboud University. (dataset). DOI: https://doi.org/10.34973/9txv-z787

[2]

Thielen, J., Marsman, P., Farquhar, J., & Desain, P. (2021). From full calibration to zero training for a code-modulated visual evoked potentials for brain–computer interface. Journal of Neural Engineering, 18(5), 056007. DOI: https://doi.org/10.1088/1741-2552/abecef

[3]

Ahmadi, S., Borhanazad, M., Tump, D., Farquhar, J., & Desain, P. (2019). Low channel count montages using sensor tying for VEP-based BCI. Journal of Neural Engineering, 16(6), 066038. DOI: https://doi.org/10.1088/1741-2552/ab4057

from moabb.datasets import Thielen2021
dataset = Thielen2021()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

30

#Chan

8

#Trials / class

5

Trials length

31.5 s

Freq

512 Hz

#Sessions

1

#Trial classes

20

#Epochs classes

2

#Epochs / class

18900 NT / 18900 T

Codes

Gold codes

Presentation rate

60 Hz

Participants

  • Population: healthy

  • Age: 25 (range: 19-62) years

Equipment

  • Amplifier: Biosemi ActiveTwo

  • Electrodes: sintered Ag/AgCl active electrodes

  • Montage: custom

  • Reference: CMS/DRL

Preprocessing

  • Data state: raw

Data Access

Experimental Protocol

  • Paradigm: cvep

  • Feedback: none

  • Stimulus: visual

Notes

Added in version 0.6.0.

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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

Examples using moabb.datasets.Thielen2021#

Dataset bubble plot

Dataset bubble plot