Doc: Minor tweaks to the Regularized CSP documentation

plugins/processing/signal-processing/doc/Doc_BoxAlgorithm_RegularizedCSPTrainer.dox-part

@@ -115,13 +115,15 @@ __________________________________________________________________
  
  Note that the usage of the CSP filters before classification training can make the cross-validation results optimistic, unless strictly non-overlapping parts of the data were used to train the CSP and the classifier (disjoint sets for each). 
  
- The trace normalization can be found in the literature [2]. Presumably the idea is to normalize the 
- scale of each chunk in order to compensate for a possible signal power drift over time during the EEG recording, 
- making each chunks' covariance contribute similarly to the aggregate regardless of the current chunks average power. 
+ The trace normalization can be found in the literature [2]. The idea is to normalize the scale of each chunk in order to 
+ compensate for a possible signal power drift over time during the EEG recording, making each chunks' covariance contribute 
+ similarly to the aggregate regardless of the current chunks average power. 
 				
  To get the "CSP with Diagonal Loading" of Lotte & Guan paper [1], set shrinkage to a positive value and Tikhonov to 0. To get the 
  "CSP with Tikhonov regularization", do the opposite. You can also try a mixture of the two. Note that the Guan & Lotte paper does not appear to use trace normalization.
  
+ To get the CSP resembling the one in the Muller-Gerkin paper, set Trace Normalization to True and the Covariance method to Chunk Average, with no regularization. Then, feed the algorithm each trial as a separate chunk (with Stimulation based epoching box). This is also the classic OV way of computing the CSP.
+ 
  Once the spatial filters are computed and saved in the configuration file, you can load the configuration into the \ref Doc_BoxAlgorithm_SpatialFilter "Spatial Filter" box.
  
  For the moment the Regularized CSP Trainer supports only two classes. 

plugins/processing/signal-processing/src/algorithms/basic/ovpCAlgorithmOnlineCovariance.cpp

@@ -127,8 +127,8 @@ OpenViBE::boolean CAlgorithmOnlineCovariance::process(void)
 
 			if(ip_bTraceNormalization) 
 			{
-				// The origin of this trick has been lost. Presumably the idea is to normalize the scale of 
-				// each chunk in order to compensate for possible signal power drift over time during the EEG recording, 
+				// This normalization can be seen e.g. Muller-Gerkin & al., 1999. Presumably the idea is to normalize the 
+				// scale of each chunk in order to compensate for possible signal power drift over time during the EEG recording, 
 				// making each chunks' covariance contribute similarly to the average regardless of
 				// the current average power. Such a normalization could also be implemented in its own 
 				// box and not done here.