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, picks=None, reject=None, flat=None, targets_from='metadata', last_target_only=True, on_missing='error', n_jobs=1, verbose='error')[source]#
Windower that creates sliding windows.
- Parameters:
concat_ds (ConcatDataset) – 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)) – Mapping from event description to target value.
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. See mne.Epochs.
reject (dict | 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 | None) – Epoch rejection parameters based on flatness of signals. If None, no rejection based on flatness is done. See mne.Epochs.
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.
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 datasets of WindowsDataset containing the extracted windows.
- Return type:
Examples using braindecode.preprocessing.create_fixed_length_windows
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Convolutional neural network regression model on fake data.

Benchmarking preprocessing with parallelization and serialization

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset