diff --git a/‎brainiak/reprsimil/brsa.py‎
Lines changed: 20 additions & 7 deletions b/‎brainiak/reprsimil/brsa.py‎
Lines changed: 20 additions & 7 deletions
diff --git a/‎tests/reprsimil/test_gbrsa.py‎
Lines changed: 7 additions & 0 deletions b/‎tests/reprsimil/test_gbrsa.py‎
Lines changed: 7 additions & 0 deletions
@@ -2818,11 +2818,13 @@ class GBRSA(BRSA):
         In this case, the standard deviation of log(SNR) is set
         by the parameter logS_range.
         If set to 'unif', a uniform prior in [0,1] is imposed.
-        In all these cases, SNR is numerically
+        In all above cases, SNR is numerically
         marginalized on a grid of parameters. So the parameter SNR_bins
         determines how accurate the numerical integration is. The more
         number of bins are used, the more accurate the numerical
         integration becomes.
+        If set to 'equal', all voxels are assumed to have the same fixed
+        SNR. Pseudo-SNR is 1.0 for all voxels.
         In all the cases, the grids used for pseudo-SNR do not really
         set an upper bound for SNR, because the real SNR is determined
         by both pseudo-SNR and U, the shared covariance structure.
@@ -3003,11 +3005,14 @@ def __init__(
         if type(logS_range) is int:
             logS_range = float(logS_range)
         self.logS_range = logS_range
-        assert SNR_prior in ['unif', 'lognorm', 'exp'], \
+        assert SNR_prior in ['unif', 'lognorm', 'exp', 'equal'], \
             'SNR_prior can only be chosen from ''unif'', ''lognorm''' \
-            ' and ''exp'''
+            ' ''exp'' and ''equal'''
         self.SNR_prior = SNR_prior
-        self.SNR_bins = SNR_bins
+        if self.SNR_prior == 'equal':
+            self.SNR_bins = 1
+        else:
+            self.SNR_bins = SNR_bins
         self.rho_bins = rho_bins
         self.tol = tol
         self.optimizer = optimizer
@@ -3094,9 +3099,14 @@ def fit(self, X, design, nuisance=None, scan_onsets=None):
         # However, in fit(), we keep the scikit-learn API that
         # X is the input data to fit and y, a reserved name not used, is
         # the label to map to from X.
-        assert self.SNR_bins >= 10 and self.rho_bins >= 10, \
+        assert self.SNR_bins >= 10 and self.SNR_prior != 'equal' or \
+            self.SNR_bins == 1 and self.SNR_prior == 'equal', \
+            'At least 10 bins are required to perform the numerical'\
+            ' integration over SNR, unless choosing SNR_prior=''equal'','\
+            ' in which case SNR_bins should be 1.'
+        assert self.rho_bins >= 10, \
             'At least 10 bins are required to perform the numerical'\
-            ' integration over SNR and rho'
+            ' integration over rho'
         assert self.logS_range * 6 / self.SNR_bins < 0.5 \
             or self.SNR_prior != 'lognorm', \
             'The minimum grid of log(SNR) should not be larger than 0.5 '\
@@ -4128,9 +4138,12 @@ def _set_SNR_grids(self):
             # Center of mass of each segment between consecutive
             # bounds are set as the grids for SNR.
             SNR_weights = np.ones(self.SNR_bins) / self.SNR_bins
-        else:  # SNR_prior == 'exp'
+        elif self.SNR_prior == 'exp':
             SNR_grids = self._bin_exp(self.SNR_bins)
             SNR_weights = np.ones(self.SNR_bins) / self.SNR_bins
+        else:
+            SNR_grids = np.ones(1)
+            SNR_weights = np.ones(1)
         SNR_weights = SNR_weights / np.sum(SNR_weights)
         return SNR_grids, SNR_weights
 
 
@@ -361,6 +361,13 @@ def test_SNR_grids():
             and np.all(np.diff(SNR_grids) > 0)
             ), 'SNR_grids or SNR_weights not correct for exponential prior'
 
+    s = brainiak.reprsimil.brsa.GBRSA(SNR_prior='equal')
+    SNR_grids, SNR_weights = s._set_SNR_grids()
+    assert (np.all(SNR_grids == 1)
+            and np.all(SNR_weights == 1)
+            and np.size(SNR_grids) == 1
+            ), 'SNR_grids or SNR_weights not correct for equal prior'
+
 
 def test_n_nureg():
     import brainiak.reprsimil.brsa