moabb.datasets.erpcore2021.ErpCore2021#

class moabb.datasets.erpcore2021.ErpCore2021(task, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: BaseDataset

Abstract base dataset class the ERP CORE dataset by Kappenman et al. 2020.

Dataset summary

#Subj

40

#Chan

30

#Trials / class

varies by ERP task

Trials length

1 s

Freq

1024 Hz

#Sessions

1

Datasets [1] from the article [2].

Dataset Description

The ERP CORE dataset includes data from 40 neurotypical young adults (25 female, 15 male; Mean years of age = 21.5, SD = 2.87, Range 18–30; 38 right handed) from the University of California. Each participant had native English competence and normal color perception, normal or corrected-to-normal vision, and no history of neurological injury or disease (as indicated by self-report). They participated in six 10-minutes optimized experiments designed to measure seven widely used ERP components: N170, Mismatch Negativity (MMN), N2pc, N400, P3, Lateralized Readiness Potential (LRP), and Error-Related Negativity (ERN). These experiments were conducted to standardize ERP paradigms and protocols across studies.

Experimental procedures: - N170: Subjects viewed faces and objects to elicit the N170 component. In this task, an image of a face, car, scrambled face, or scrambled car was presented on each trial in the center of the screen, and participants responded whether the stimulus was an “object” (face or car) or a “texture” (scrambled face or scrambled car). - MMN: Subjects were exposed to a sequence of auditory stimuli to evoke the mismatch negativity response, indicating automatic detection of deviant sounds. Standard tones (presented at 80 dB, with p = .8) and deviant tones (presented at 70 dB, with p = .2) were presented over speakers while participants watched a silent video and ignored the tones. - N2pc: Participants were given a target color of pink or blue at the beginning of a trial block, and responded on each trial whether the gap in the target color square was on the top or bottom. - N400: On each trial, a red prime word was followed by a green target word. Participants responded whether the target word was semantically related or unrelated to the prime word. - P3: The letters A, B, C, D, and E were presented in random order (p = .2 for each letter). One letter was designated the target for a given block of trials, and the other 4 letters were non-targets. Thus, the probability of the target category was .2, but the same physical stimulus served as a target in some blocks and a nontarget in others. Participants responded whether the letter presented on each trial was the target or a non-target for that block. - LRP & ERN: A central arrowhead pointing to the left or right was flanked on both sides by arrowheads that pointed in the same direction (congruent trials) or the opposite direction (incongruent trials). Participants indicated the direction of the central arrowhead on each trial with a left- or right-hand buttonpress.

The continuous EEG was recorded using a Biosemi ActiveTwo recording system with active electrodes (Biosemi B.V., Amsterdam, the Netherlands). Recording from 30 scalp electrodes, mounted in an elastic cap and placed according to the International 10/20 System (FP1, F3, F7, FC3, C3, C5, P3, P7, P9, PO7, PO3, O1, Oz, Pz, CPz, FP2, Fz, F4, F8, FC4, FCz, Cz, C4, C6, P4, P8, P10, PO8, PO4, O2; see Supplementary Fig. S1). The common mode sense (CMS) electrode was located at PO1, and the driven right leg (DRL) electrode was located at PO2. The horizontal electrooculogram (HEOG) was recorded from electrodes placed lateral to the external canthus of each eye. The vertical electrooculogram (VEOG) was recorded from an electrode placed below the right eye. Signals were incidentally also recorded from 37 other sites, but these sites were not monitored during the recording and are not included in the ERP CORE data set. All signals were low-pass filtered using a fifth order sinc filter with a half-power cutoff at 204.8 Hz and then digitized at 1024 Hz with 24 bits of resolution. The signals were recorded in single-ended mode (i.e., measuring the voltage between the active and ground electrodes without the use of a reference), and referencing was performed offline.

References

[1]

Emily S. Kappenman, Jaclyn L. Farrens, Wendy Zhang, Andrew X. Stewart, Steven J. Luck. (2020). ERP CORE: An open resource for human event-related potential research. NeuroImage. DOI: https://doi.org/10.18115/D5JW4R

[2]

Emily S. Kappenman, Jaclyn L. Farrens, Wendy Zhang, Andrew X. Stewart, Steven J. Luck. ERP CORE: An open resource for human event-related potential research. DOI: https://doi.org/10.1016/j.neuroimage.2020.117465

__init__(task, subjects=None, sessions=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])

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 BIDS paths of a single subject.

Parameters:

  • subject (int) – The subject number to fetch data for.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_(dataset) 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.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

A list containing the BIDSPath object for the subject’s data file.

Return type:

list

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()).

download_by_subject(subject, path=None)[source]#

Download and extract the dataset.

Parameters:

  • subject (int) – The subject number to download the dataset for.

  • path (str | None) – The path to the directory where the dataset should be downloaded. If None, the default directory is used.

Returns:

path – The dataset path.

Return type:

str

abstract static encode_event(row: str)[source]#

Encode a single event values based on the task-specific criteria.

Parameters:

row (pandas.Series) – A row of the events DataFrame.

Returns:

Encoded event value.

Return type:

str

abstract encoding(events_df)[source]#

Encode the column value in the events DataFrame.

Parameters:

events_df (pandas.DataFrame) – DataFrame containing the events information.

Returns:

A tuple containing the encoded event values and the mapping dictionary.

Return type:

tuple

events_path(subject)[source]#

Get the path to the events file for a given subject.

Parameters:

subject (int) – The subject number for which to get the events file path.

Returns:

The path to the events file.

Return type:

str

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

handle_events_reading(events_path, raw)[source]#

Read associated events.tsv and populate raw with annotations.

Parameters:

  • events_path (str) – The path to the events file.

  • raw (mne.io.Raw) – The raw EEG data object.

Returns:

The updated raw EEG data object with annotations.

Return type:

mne.io.Raw

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