braindecode.datasets.BaseConcatDataset

class braindecode.datasets.BaseConcatDataset(list_of_ds, target_transform=None, *, lazy=False)

A base class for concatenated datasets.

Holds either mne.Raw or mne.Epoch in self.datasets and has a pandas DataFrame with additional description.

Includes Hugging Face Hub integration via HubDatasetMixin for uploading and downloading datasets.

Parameters:

• list_of_ds (list) – list of RecordDataset

• target_transform (callable | None) – Optional function to call on targets before returning them.

• lazy (bool, default False) – If True, defer computing cumulative sizes until length or item access.

Methods

get_metadata()

Concatenate the metadata and description of the wrapped Epochs.

Returns:

metadata – DataFrame containing as many rows as there are windows in the BaseConcatDataset, with the metadata and description information for each window.

Return type:

pd.DataFrame

save(path, overwrite=False, offset=0)

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

• path (str) –

  Directory in which subdirectories are created to store

  -raw.fif | -epo.fif and .json files to.

• overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

• offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

set_description(description, overwrite=False)

Update (add or overwrite) the dataset description.

Parameters:

• description (dict | pd.DataFrame) – Description in the form key: value where the length of the value has to match the number of datasets.

• overwrite (bool) – Has to be True if a key in description already exists in the dataset description.

set_target(column)

Use column as the target y for every subdataset.

Dispatches on the subdataset type:

• For WindowsDataset / EEGWindowsDataset, column is looked up in per-window metadata first, then in the per-record description (broadcast to every window). The resolved values overwrite ds.metadata['target'] and ds.y. For WindowsDataset, the underlying ds.windows.metadata is kept in sync so get_metadata() and the repr reflect the new target.

• For RawDataset, column must exist on the description. ds.target_name is set to column so __getitem__ reads description[column] as y on every access — no rebuild needed.

Parameters:

column (Hashable) – Name of a metadata column or description field (BIDS entity, participants.tsv extra, …). Typically a string, but any hashable that pandas accepts as a column label is allowed.

Returns:

self

Return type:

BaseConcatDataset

Raises:

• TypeError – If any subdataset is not a WindowsDataset, EEGWindowsDataset, or RawDataset, or if a windowed subdataset has lazy (non-DataFrame) metadata.

• ValueError – If column is not present on a subdataset’s metadata or description, or if a windowed subdataset has targets_from='channels' (which would make this a silent no-op since __getitem__ reads y from misc channels, not from metadata['target']).

split(by=None, property=None, split_ids=None)

Split the dataset based on information listed in its description.

The format could be based on a DataFrame or based on indices.

Parameters:

• by (str | list | dict) – If by is a string, splitting is performed based on the description DataFrame column with this name. If by is a (list of) list of integers, the position in the first list corresponds to the split id and the integers to the datapoints of that split. If a dict then each key will be used in the returned splits dict and each value should be a list of int.

• property (str) –

  Deprecated

  Some property which is listed in the info DataFrame.

• split_ids (list | dict) –

  Deprecated

  List of indices to be combined in a subset. It can be a list of int or a list of list of int.

Returns:

splits – A dictionary with the name of the split (a string) as key and the dataset as value.

Return type:

dict

to_epochs_dataset()

Converts this BaseConcatDataset such that all datasets are WindowsDataset with mne.Epochs.

In Braindecode, the data can either be stored as mne.io.Raw (in EEGWindowsDataset) or as mne.Epochs (in WindowsDataset). This function converts all the underlying datasets to WindowsDataset with mne.Epochs. This can be useful for reducing disk space when you want to save a dataset.

Returns:

A new BaseConcatDataset where all datasets are WindowsDataset with mne.Epochs.

Return type:

BaseConcatDataset[WindowsDataset]

Raises:

ValueError – If any of the underlying datasets is a RawDataset or any other type that is not EEGWindowsDataset or WindowsDataset, as they cannot be converted to epochs.

Examples using braindecode.datasets.BaseConcatDataset

Cleaning EEG Data with EEGPrep for Trialwise Decoding

Cleaning EEG Data with EEGPrep for Trialwise Decoding