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)[source]#

Create WindowsDatasets from mne.RawArrays.

Parameters:

  • raws (list[BaseRaw]) – 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 (list[dict | Series] | None) – list of dicts or pandas.Series with additional information about the raws

  • 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) – 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) – 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 – X and y transformed to a dataset format that is compatible with skorch and braindecode

Return type:

BaseConcatDataset

Examples using braindecode.datasets.create_from_mne_raw#

MNE Dataset Example

MNE Dataset Example