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

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).

windows_datasets: BaseConcatDataset

X and y transformed to a dataset format that is compativle with skorch and braindecode

Examples using braindecode.datasets.create_from_mne_raw