Method fit

mne/decoding/csp.py:246–278

Estimate the CSP decomposition on epochs. Parameters ---------- X : ndarray, shape (n_epochs, n_channels, n_times) The data on which to estimate the CSP. y : array, shape (n_epochs,) The class for each epoch. Returns -------

(self, X, y)

        _validate_type(self.cov_method_params, (abc.Mapping, None), "cov_method_params")

    def fit(self, X, y):
        """Estimate the CSP decomposition on epochs.

        Parameters
        ----------
        X : ndarray, shape (n_epochs, n_channels, n_times)
            The data on which to estimate the CSP.
        y : array, shape (n_epochs,)
            The class for each epoch.

        Returns
        -------
        self : instance of CSP
            Returns the modified instance.
        """
        X, y = self._check_data(X, y=y, fit=True, return_y=True)
        self._validate_params(y=y)

        # Covariance estimation, GED/AJD
        # and evecs/evals sorting happen here
        super().fit(X, y)

        pick_filters = self.filters_[: self.n_components]
        X = np.asarray([np.dot(pick_filters, epoch) for epoch in X])

        # compute features (mean power)
        X = (X**2).mean(axis=2)

        # To standardize features
        self.mean_ = X.mean(axis=0)
        self.std_ = X.std(axis=0)

        return self

    def transform(self, X):
        """Estimate epochs sources given the CSP filters.