braindecode.preprocessing.create_fixed_length_windows#
- braindecode.preprocessing.create_fixed_length_windows(concat_ds: BaseConcatDataset, start_offset_samples: int = 0, stop_offset_samples: int | None = None, window_size_samples: int | None = None, window_stride_samples: int | None = None, drop_last_window: bool | None = None, mapping: dict[str, int] | None = None, preload: bool = False, picks: str | Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | slice | None = None, reject: dict[str, float] | None = None, flat: dict[str, float] | None = None, targets_from: str = 'metadata', last_target_only: bool = True, lazy_metadata: bool = False, on_missing: str = 'error', n_jobs: int = 1, verbose: bool | str | int | None = '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.
lazy_metadata (bool) – If True, metadata is not computed immediately, but only when accessed by using the _LazyDataFrame (experimental).
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#
Convolutional neural network regression model on fake data.
Benchmarking preprocessing with parallelization and serialization
Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset
