fix multiplication order in original SSD

Author: Genuster

mne/decoding/base.py

@@ -129,14 +129,8 @@ def fit(self, X, y=None):
             else:
                 S = covs[0]
                 R = covs[1]
-                if self.restr_type == "ssd":
-                    mult_order = "ssd"
-                else:
-                    mult_order = None
                 restr_mat = _handle_restr_mat(C_ref, self.restr_type, info, rank)
-                evals, evecs = _smart_ged(
-                    S, R, restr_mat, R_func=self.R_func, mult_order=mult_order
-                )
+                evals, evecs = _smart_ged(S, R, restr_mat, R_func=self.R_func)
 
             evals, evecs = self.mod_ged_callable(
                 evals, evecs, covs, **self.mod_params, **kwargs
@@ -151,18 +145,12 @@ def fit(self, X, y=None):
         elif self.dec_type == "multi":
             self.classes_ = np.unique(y)
             R = covs[-1]
-            if self.restr_type == "ssd":
-                mult_order = "ssd"
-            else:
-                mult_order = None
             restr_mat = _handle_restr_mat(C_ref, self.restr_type, info, rank)
             all_evals, all_evecs, all_patterns = list(), list(), list()
             for i in range(len(self.classes_)):
                 S = covs[i]
 
-                evals, evecs = _smart_ged(
-                    S, R, restr_mat, R_func=self.R_func, mult_order=mult_order
-                )
+                evals, evecs = _smart_ged(S, R, restr_mat, R_func=self.R_func)
 
                 evals, evecs = self.mod_ged_callable(
                     evals, evecs, covs, **self.mod_params, **kwargs

mne/decoding/ged.py

@@ -34,7 +34,7 @@ def _handle_restr_mat(C_ref, restr_type, info, rank):
     return restr_mat
 
 
-def _smart_ged(S, R, restr_mat=None, R_func=None, mult_order=None):
+def _smart_ged(S, R, restr_mat=None, R_func=None):
     """Perform smart generalized eigenvalue decomposition (GED) of S and R.
 
     If restr_mat is provided S and R will be restricted to the principal subspace
@@ -47,12 +47,8 @@ def _smart_ged(S, R, restr_mat=None, R_func=None, mult_order=None):
         evals, evecs = scipy.linalg.eigh(S, R)
         return evals, evecs
 
-    if mult_order == "ssd":
-        S_restr = restr_mat @ (S @ restr_mat.T)
-        R_restr = restr_mat @ (R @ restr_mat.T)
-    else:
-        S_restr = restr_mat @ S @ restr_mat.T
-        R_restr = restr_mat @ R @ restr_mat.T
+    S_restr = restr_mat @ S @ restr_mat.T
+    R_restr = restr_mat @ R @ restr_mat.T
     if R_func is not None:
         R_restr = R_func([S_restr, R_restr])
     evals, evecs_restr = scipy.linalg.eigh(S_restr, R_restr)

mne/decoding/ssd.py

@@ -460,6 +460,6 @@ def _dimensionality_reduction(cov_signal, cov_noise, info, rank):
         logger.info("Preserving covariance rank (%i)", rank)
 
     # project covariance matrices to rank subspace
-    cov_signal = np.matmul(rank_proj.T, np.matmul(cov_signal, rank_proj))
-    cov_noise = np.matmul(rank_proj.T, np.matmul(cov_noise, rank_proj))
+    cov_signal = rank_proj.T @ cov_signal @ rank_proj
+    cov_noise = rank_proj.T @ cov_noise @ rank_proj
     return cov_signal, cov_noise, rank_proj