braindecode.datautil.infer_signal_properties#
- braindecode.datautil.infer_signal_properties(X, y=None, mode='classification', classes=None)[source]#
Infers signal properties from the data.
The extracted signal properties are:
n_chans: number of channels
n_times: number of time points
n_outputs: number of outputs
chs_info: channel information
sfreq: sampling frequency
The returned dictionary can serve as kwargs for model initialization.
Depending on the type of input passed, not all properties can be inferred.
- Parameters:
X (array-like or mne.BaseEpochs or Dataset) – Input data
y (array-like or None) – Targets
mode (
Literal['classification','regression']) – Mode of the task
- Returns:
signal_kwargs – Dictionary with signal-properties. Can serve as kwargs for model initialization.
- Return type:
dict[Literal['n_outputs','n_chans','chs_info','n_times','input_window_seconds','sfreq'],Any]
