diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex
index aad9bd2..8e4f2df 100644
--- a/text/thesis/02MaterialsAndMethods.tex
+++ b/text/thesis/02MaterialsAndMethods.tex
@@ -66,6 +66,7 @@
 
         EMG is mainly developed for diagnostic tasks. However it is also applicable in science to track muscle activity. %TODO?
     \subsection{Synergies}
+    \label{back:synergies}
         Movement of the arm (and other parts of the body) are under-determined meaning with given trajectory there are different muscle contractions possible. One idea how this problem could be solved by our nervous system are synergies. Proposed by Bernstein in 1967 (\cite{Bernstein67}) they describe the goal of the movement (e.g. the trajectory) instead of controlling single muscles. This would mean however that predicting the activity of single muscles from EEG is harder than predicting a synergy which in turn determines the contraction of muscles.\\
         Evidence for the use of synergies in the nervous system was found e.g. by Bizzi et al. (\cite{Bizzi08}) and Byadarhaly et al. (\cite{Byadarhaly12}). They also showed that synergies meet the necessary requirement to be able to build predictable trajectories.\\
         Synergies are usually gotten from EMG signal through a principal component analysis (PCA, cf. \ref{mat:pca}), non-negative matrix factorization (NMF, cf. \ref{mat:nmf}) or autoencoders (a form of neuronal network, cf. \ref{mat:autoenc}).
@@ -327,7 +328,14 @@
         As last step we adjust the length of the stimulus-vector to the length of the EEG data.\\
         According to this classification we take only data in the further analysis which are classified different than -1 meaning they are either clear rest or clear movement.
     \subsection{Synergies}
-%TODO
+        Synergies we generate based on different options for dimensionality reduction (cf. \ref{back:synergies}).\\
+        EMG data (as wave length) is reduced to $n$ dimensions, where $n$ is the desired number of Synergies.\\
+        Using PCA this is done by taking the first $n$ components. Then the EMG data is transformed into the $n$-dimensional space spanned by the components.\\
+        NMF is done with $n$ as inner dimension. Then EMG data is multiplied with the resulting matrix to transform it to $n$-dimensional data.\\
+        Eventually autoencoders are trained with a hidden layer of size $n$ and afterwards EMG data is encoded with the learned weights. This is equivalent to taking the hidden layer activity for the corresponding time step.\\
+        Since synergies are generated from EMG they have the same dimensionality in the first dimension\footnote{only depending on window size and shift for EMG data and the recoding duration} and $n$ in the second.
+    \subsection{Kinematics}
+    %TODO
 \section{Data Analysis}
     \subsection{Classification}
         Classification can be done in different ways. First approach was discriminating Movement from Rest. This was done by training an SVM and testing its results with 10-fold cross validation. This was done with EMG, EEG and LF data. EMG in this setting is trivial since it was the basis for the classification (cf. \ref{sec:newClass}).\\
diff --git a/text/thesis/outline.txt b/text/thesis/outline.txt
index eb7ed88..0229c94 100644
--- a/text/thesis/outline.txt
+++ b/text/thesis/outline.txt
@@ -44,6 +44,9 @@
     - Ridge
 
 ## Results
+- no Synergies
+- topoplot
+- ...
 
 ## Discussion