braindecode.datasets.create_from_mne_epochs#
- braindecode.datasets.create_from_mne_epochs(list_of_epochs, window_size_samples, window_stride_samples, drop_last_window, descriptions=None, mapping=None, preload=False, picks=None, drop_bad_windows=True)[source]#
Create WindowsDatasets from mne.Epochs.
- Parameters:
list_of_epochs (
list[BaseEpochs]) – list of mne.Epochswindow_size_samples (
int) – window sizewindow_stride_samples (
int) – stride between windowsdrop_last_window (
bool) – whether or not have a last overlapping window, when windows do not equally divide the continuous signaldescriptions (
list[dict|Series] |None) – list of dicts or pandas.Series with additional information about the epochs. If None, no description is added. Length must matchlist_of_epochs. Each description is applied to all windows generated from the corresponding Epochs object.mapping (
dict[str|int,int] |None) – Mapping from event description to target value. Keys can be integers (matchingepochs.events[:, 2]) or their string representations. If None, targets are set to the raw integer event codes. If a mapping is provided and an event code is not found in the mapping, the original event code is kept as-is.preload (
bool) – if True, preload the data of the Epochs objects.picks (
str|list|slice|None) – channels to include. If None, all available channels are used.drop_bad_windows (
bool) – If True, call .drop_bad() on the resulting mne.Epochs object. This allows identifying windows that fall outside of the valid signal range. Defaults to True.
- Returns:
windows_datasets – X and y transformed to a dataset format that is compatible with skorch and braindecode
- Return type:
