diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex
index fce3a3b..17fe984 100644
--- a/text/thesis/02MaterialsAndMethods.tex
+++ b/text/thesis/02MaterialsAndMethods.tex
@@ -319,7 +319,7 @@
             \end{table}
         \subsubsection{States}
             The main information contained by the \texttt{states} \matlab{}\texttt{-struct} is the currently presented stimulus. The \texttt{struct} has same length as the signal so that for every entry in the signal there is corresponding state information.\\
-            There were some adjustments necessary since it did not match the movements (cf. Section~\ref{sec:newClass}).
+            There were some adjustments necessary since it did not match the movements (cf. Section~\ref{mm:newClass}).
         \subsubsection{Parameters}
             All the settings from the BCI2000 recording are saved in and loaded from the \texttt{parameters}.\\
             Examples are the names of the channels, the random seed for BCI2000 and the sounds, meaning and duration for different stimuli.
@@ -392,7 +392,8 @@
     \subsection{Classification}
         Classification can be done in different ways. First approach is discriminating Movement from Rest. This is done by training an SVM and testing its results with 10-fold cross validation. We do this with EMG, EEG and LF data. EMG in this setting is trivial since it was the basis for the classification (cf. \ref{mm:newClass}).\\
         In a second step we try to discriminate movement in different directions also with an SVM trained on EMG, EEG or LF data respectively. The fit of the model is also checked with 10-fold cross validation.\\
-        For unbiased classification it is necessary to train with equally sized classes. For that purpose and to lower computation time we only take in 250 (as default) samples per class (cf. Comparison of results in Section~\ref{res:maxPerClass}).\\
+        For unbiased classification it is necessary to train with equally sized classes. For that purpose and to lower computation time we only take in 250 (as default) samples per class%TODO?: (cf. Comparison of results in Section~\ref{res:maxPerClass})
+        .\\
         The parameter $c$ for the support vector machine is found with an additional step of cross validation or set to 1. (Results in Section~\ref{res:maxC}).\\
         To learn about biased classifications and about similar brain activity for different movements the confusion matrix is created (cf. Section~\ref{mm:cm}).\\
         The resulting accuracy is the mean of each of the 10 cross validation steps.
diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex
index 8dd9dd0..623f1ba 100644
--- a/text/thesis/03Results.tex
+++ b/text/thesis/03Results.tex
@@ -62,6 +62,7 @@
             \label{fig:overviewLF}
         \end{figure}
     \subsection{Trade-off parameter}
+    \label{res:maxC}
         With a cross validation we compare the results for the soft-margin parameter for $\lambda=0.1,1,10$. The results are shown in figure~\ref{fig:svmCV}.\\
         TODO%TODO
     \subsection{Confusion Matrices}
diff --git a/text/thesis/Bfunctions.tex b/text/thesis/Bfunctions.tex
index 47b5560..2ecf700 100644
--- a/text/thesis/Bfunctions.tex
+++ b/text/thesis/Bfunctions.tex
@@ -99,8 +99,15 @@
     \subsection{\texttt{mySaveFigure.m}}
         \texttt{mySaveFigure.m} save a given figure with default size at given filename.
 \section{Bash Scripts}
-\label{code:noSyn.bash}
-\label{code:run.bash}
+    \subsection{\texttt{noOfSyn.bash}}
+    \label{code:noSyn.bash}
+        Starts \texttt{noSynergies.m} and pipes output to logfile.
+    \subsection{\texttt{runOnEciton.bash}}
+    \label{code:run.bash}
+        Creates \texttt{dirs.txt} containing a list of sessions, then starts \texttt{callAll.m} and pipes output to logfile.
+    \subsection{\texttt{runOnEcitonPos.bash}}
+    \label{code:runPos.bash}
+        Same as \ref{code:run.bash} but starts \texttt{callAllPos.m}.
 \section{miscellaneous}
     \subsection{\texttt{psdPlot.m}}
         Plots PSD with pburg and pwelch.