diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex
index c9bb1b5..6dde9a6 100644
--- a/text/thesis/02MaterialsAndMethods.tex
+++ b/text/thesis/02MaterialsAndMethods.tex
@@ -23,7 +23,7 @@
         \begin{figure}[!p]
             \centering
             \includegraphics[width=\textwidth]{eeg_electrodes_10-20.png}
-            \caption{Full 10-20 system}
+            \caption{Naming according to 10-20 system (Source: Marius 't Hart)}
             \label{fig:10-20}
         \end{figure}
         \subsubsection{Frequency bands}
diff --git a/text/thesis/thesis.tex b/text/thesis/thesis.tex
index e5bf9e1..f414b5d 100644
--- a/text/thesis/thesis.tex
+++ b/text/thesis/thesis.tex
@@ -114,7 +114,8 @@
 
 \section*{Abstract}
 \addcontentsline{toc}{section}{Abstract}
-In this thesis we show the plausibility of synergies as an intermediate step between brain and muscles. Our results show only small decrease in predicting performance for position and velocity compared to the EMG signal. This was achieved with synergies acquired through dimensionality reduction from EMG signal.\\
+Synergies are %TODO
+This thesis shows the plausibility of synergies as an intermediate step between brain and muscles. Our results show only small decrease in predicting performance for position and velocity compared to the EMG signal. This was achieved with synergies acquired through dimensionality reduction from EMG signal.\\
 The results of prediction of, via and from synergies are compared with other techniques currently used to predict movement from EEG in a classification and regression context. Over all synergies perform not much worse than EMG and are predicted better from EEG.\\
 We also compare different methods for the acquisition of synergies. Our findings show that autoencoders are a great possibility to generate synergies from EMG. Synergies from non-Negative Matrix Factorization also perform well, those acquired by Principal Component Analysis are performing worse when being predicted from EEG.