braindecode.preprocessing.create_fixed_length_windows#

braindecode.preprocessing.create_fixed_length_windows(concat_ds, start_offset_samples=0, stop_offset_samples=None, window_size_samples=None, window_stride_samples=None, drop_last_window=None, mapping=None, preload=False, drop_bad_windows=None, picks=None, reject=None, flat=None, targets_from='metadata', last_target_only=True, lazy_metadata=False, on_missing='error', use_mne_epochs=None, n_jobs=1, verbose='error')[source]#

Windower that creates sliding windows.

Parameters:

concat_ds (BaseConcatDataset[RawDataset]) – A concat of base datasets each holding raw and description.
start_offset_samples (int) – Start offset from beginning of recording in samples.
stop_offset_samples (int | None) – Stop offset from beginning of recording in samples. If None, set to be the end of the recording.
window_size_samples (int | None) – Window size in samples. If None, set to be the maximum possible window size, ie length of the recording, once offsets are accounted for.
window_stride_samples (int | None) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so windows will not overlap.
drop_last_window (bool | None) – Whether or not have a last overlapping window, when windows do not equally divide the continuous signal. Must be set to a bool if window size and stride are not None.
mapping (dict[str, int] | None) – Mapping from event description to target value.
preload (bool) – If True, preload the data of the Epochs objects.
drop_bad_windows (bool | None) – If True, call .drop_bad() on the resulting mne.Epochs object. This step allows identifying e.g., windows that fall outside of the continuous recording. It is suggested to run this step here as otherwise the BaseConcatDataset has to be updated as well. Only has an effect if mne Epochs are created (i.e. use_mne_epochs=True).
picks (str | TypeAliasType | slice | None) – Channels to include. If None, all available channels are used. See mne.Epochs.
reject (dict[str, float] | None) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no rejection is done based on peak-to-peak amplitude. See mne.Epochs.
flat (dict[str, float] | None) – Epoch rejection parameters based on flatness of signals. If None, no rejection based on flatness is done. See mne.Epochs.
lazy_metadata (bool) – If True, metadata is not computed immediately, but only when accessed by using the _LazyDataFrame (experimental). Cannot be used together with use_mne_epochs=True.
on_missing (str) – What to do if one or several event ids are not found in the recording. Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.
use_mne_epochs (bool | None) – If False, return EEGWindowsDataset objects. If True, return mne.Epochs objects encapsulated in WindowsDataset objects, which is substantially slower than EEGWindowsDataset. If None, it will be inferred from the other parameters: True if any of reject, picks, or flat is set, or if drop_bad_windows is True; False otherwise. If use_mne_epochs is inferred as True and drop_bad_windows is None, it is treated as True.
n_jobs (int) – Number of jobs to use to parallelize the windowing.
verbose (bool | str | int | None) – Control verbosity of the logging output when calling mne.Epochs.

Returns:

windows_datasets – Concatenated dataset containing either WindowsDataset or EEGWindowsDataset objects with the extracted windows, depending on the value of use_mne_epochs.

Return type: