braindecode.preprocessing.filterbank

braindecode.preprocessing.filterbank(raw: BaseRaw, frequency_bands: list[tuple[float, float]], drop_original_signals: bool = True, order_by_frequency_band: bool = False, **mne_filter_kwargs)

Applies multiple bandpass filters to the signals in raw. The raw will be modified in-place and number of channels in raw will be updated to len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if drop_original_signals).

Parameters:

• raw (mne.io.Raw) – The raw signals to be filtered.

• frequency_bands (list(tuple)) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).

• drop_original_signals (bool) – Whether to drop the original unfiltered signals

• order_by_frequency_band (bool) – If True will return channels ordered by frequency bands, so if there are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].

• mne_filter_kwargs (dict) – Keyword arguments for filtering supported by mne.io.Raw.filter(). Please refer to mne for a detailed explanation.