use mne's pinv in SSD and Xdawn instead of np.linalg.pinv

Author: Genuster

mne/decoding/base.py

@@ -143,10 +143,7 @@ def fit(self, X, y=None):
             evals, evecs = mod_ged_callable(evals, evecs, covs, **kwargs)
             self.evals_ = evals
             self.filters_ = evecs.T
-            if self.restr_type == "ssd":
-                self.patterns_ = np.linalg.pinv(evecs)
-            else:
-                self.patterns_ = pinv(evecs)
+            self.patterns_ = pinv(evecs)
 
         elif self.dec_type == "multi":
             self.classes_ = np.unique(y)
@@ -161,7 +158,7 @@ def fit(self, X, y=None):
                 evals, evecs = mod_ged_callable(evals, evecs, covs, **kwargs)
                 all_evals.append(evals)
                 all_evecs.append(evecs.T)
-                all_patterns.append(np.linalg.pinv(evecs))
+                all_patterns.append(pinv(evecs))
             self.evals_ = np.array(all_evals)
             self.filters_ = np.array(all_evecs)
             self.patterns_ = np.array(all_patterns)

mne/decoding/ssd.py

@@ -21,6 +21,7 @@
     _verbose_safe_false,
     fill_doc,
     logger,
+    pinv,
 )
 from ._covs_ged import _ssd_estimate
 from ._mod_ged import _ssd_mod
@@ -271,7 +272,7 @@ def fit(self, X, y=None):
         self.eigvals_ = eigvals_[ix]
         # project back to sensor space
         self.filters_ = np.matmul(rank_proj, eigvects_[:, ix])
-        self.patterns_ = np.linalg.pinv(self.filters_)
+        self.patterns_ = pinv(self.filters_)
         # Need to unify with Xdawn and CSP as they store it as (n_components, n_chs)
         self.filters_ = self.filters_.T
 

mne/preprocessing/xdawn.py

@@ -208,7 +208,7 @@ def _fit_xdawn(
             )
         evecs = evecs[:, np.argsort(evals)[::-1]]  # sort eigenvectors
         evecs /= np.apply_along_axis(np.linalg.norm, 0, evecs)
-        _patterns = np.linalg.pinv(evecs.T)
+        _patterns = pinv(evecs.T)
         filters.append(evecs[:, :n_components].T)
         patterns.append(_patterns[:, :n_components].T)