braindecode.datasets.create_from_mne_raw¶
- braindecode.datasets.create_from_mne_raw(raws, trial_start_offset_samples, trial_stop_offset_samples, window_size_samples, window_stride_samples, drop_last_window, descriptions=None, mapping=None, preload=False, drop_bad_windows=True, accepted_bads_ratio=0.0)¶
Create WindowsDatasets from mne.RawArrays
- Parameters
- raws: array-like
list of mne.RawArrays
- trial_start_offset_samples: int
start offset from original trial onsets in samples
- trial_stop_offset_samples: int
stop offset from original trial stop in samples
- window_size_samples: int
window size
- window_stride_samples: int
stride between windows
- drop_last_window: bool
whether or not have a last overlapping window, when windows do not equally divide the continuous signal
- descriptions: array-like
list of dicts or pandas.Series with additional information about the raws
- mapping: dict(str: int)
mapping from event description to target value
- preload: bool
if True, preload the data of the Epochs objects.
- drop_bad_windows: bool
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.
- accepted_bads_ratio: float, optional
Acceptable proportion of trials withinconsistent length in a raw. If the number of trials whose length is exceeded by the window size is smaller than this, then only the corresponding trials are dropped, but the computation continues. Otherwise, an error is raised. Defaults to 0.0 (raise an error).
- Returns
- windows_datasets: BaseConcatDataset
X and y transformed to a dataset format that is compativle with skorch and braindecode
