moabb.datasets.base.BaseDataset#

class moabb.datasets.base.BaseDataset(subjects, sessions_per_subject, events, code, interval, paradigm, doi=None, unit_factor=1000000.0)[source]#

Abstract Moabb BaseDataset.

Parameters required for all datasets

Parameters:

subjects (List of int) – List of subject number (or tuple or numpy array)
sessions_per_subject (int) – Number of sessions per subject (if varying, take minimum)
events (dict of strings) – String codes for events matched with labels in the stim channel. Currently imagery codes codes can include: - left_hand - right_hand - hands - feet - rest - left_hand_right_foot - right_hand_left_foot - tongue - navigation - subtraction - word_ass (for word association)
code (string) – Unique identifier for dataset, used in all plots. The code should be in CamelCase.
interval (list with 2 entries) – Imagery interval as defined in the dataset description
paradigm (['p300','imagery', 'ssvep']) – Defines what sort of dataset this is
doi (DOI for dataset, optional (for now))

abstract data_path(subject, path=None, force_update=False, update_path=None, verbose=None) → list[str | Path][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_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: