Estimate epochs sources given the SPoC filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data. Returns ------- X : ndarray If self.transform_into == 'average_power' then returns the power of
(self, X)
| 866 | return self |
| 867 | |
| 868 | def transform(self, X): |
| 869 | """Estimate epochs sources given the SPoC filters. |
| 870 | |
| 871 | Parameters |
| 872 | ---------- |
| 873 | X : array, shape (n_epochs, n_channels, n_times) |
| 874 | The data. |
| 875 | |
| 876 | Returns |
| 877 | ------- |
| 878 | X : ndarray |
| 879 | If self.transform_into == 'average_power' then returns the power of |
| 880 | CSP features averaged over time and shape (n_epochs, n_components) |
| 881 | If self.transform_into == 'csp_space' then returns the data in CSP |
| 882 | space and shape is (n_epochs, n_components, n_times). |
| 883 | """ |
| 884 | return super().transform(X) |
| 885 | |
| 886 | def fit_transform(self, X, y=None, **fit_params): |
| 887 | """Fit SPoC to data, then transform it. |