diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex
index b7e2895..c9bb1b5 100644
--- a/text/thesis/02MaterialsAndMethods.tex
+++ b/text/thesis/02MaterialsAndMethods.tex
@@ -399,9 +399,9 @@
         The resulting accuracy is the mean of each of the 10 cross validation steps.
     \subsection{Predicting Kinematics}
         The prediction of kinematics is done with ridge regression. Since there are more data for kinematics than for EEG we use the mean position or movement and predict these.\\
-        The regression is done in 10-fold cross validation for each dimension ($x,y,\theta$). The parameter $\lambda$ (cf. ~\ref{mm:ridge}) is ascertained with an additional cross validation. The resulting correlation is the mean correlation of each of the 10 parts with the best parameter lambda each. The correlation for the dimensions are calculated independently.
+        The regression is done in 10-fold cross validation for each dimension ($x,y,\theta$) and the parameter $\lambda$ (cf. ~\ref{mm:ridge}) is ascertained with an additional cross validation. The resulting correlation is the mean correlation of each of the 10 parts with the best parameter lambda each while the correlation for each dimension is calculated independently.
     \subsection{Predicting Synergies}
-        Predicting Synergies works similar as for the kinematics. Only change is that the synergies may have other dimension. Nevertheless each synergy is predicted from all EEG data as one output and correlation is calculated for each synergy.
+        Predicting synergies works similar as for the kinematics. Only change is that the synergies may have other dimensionality. Nevertheless each synergy is predicted from all EEG data as one output and correlation is calculated for each synergy.
     \subsection{Predicting EMG}
         When predicting EMG data we use the sum of the waveform length in the time corresponding to the EEG data. As the EMG data was summed to gain the data for our use this is a reasonable approach.\\
         The remaining steps are the same as for kinematics and Synergies.
@@ -412,7 +412,7 @@
     \subsection{Pause}
     \label{mat:pause}
         We introduce a pause before movement onset. This pause means that 1 second before movement onset is not taken into account when analyzing the data. If there is no pause we only take 1s to 0.5 second before movement onset out and classify the last 0.5 seconds before movement as belonging to the following task.\\
-        This was necessary since the data about presentation of stimuli didn't match the recordings and we had to reclassify (cf. section \ref{mm:newClass}).
+        This was necessary since the data about presentation of stimuli did not match the recordings and we had to reclassify (cf. section \ref{mm:newClass}).
     \subsection{Prediction with interim step}
         All these analyses only show the accuracy of one step. To get a measure for the over-all performance we predict synergies from EEG and use them to predict EMG or kinematics respectively.\\
         The resulting correlation is the mean of the correlations of a 10-fold cross validation where the same unknown synergies are predicted from EEG and used to predict EMG or kinematics. So there is no correction step between the steps and EMG or kinematics are predicted from EEG via the Synergies. Here also different methods to determine Synergies are compared (see Section~\ref{res:differentSynergiesVia}).
diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex
index f05f715..dd3a4d3 100644
--- a/text/thesis/03Results.tex
+++ b/text/thesis/03Results.tex
@@ -11,7 +11,7 @@
         \label{fig:noSyn}
     \end{figure}%TODO (last): check orientation of figure (bottom should be outer edge)
     When comparing the results of prediction via different number of synergies, 2 synergies perform significantly ($p<0.01$) worse than 3 and 4. Between 3 and 4 synergies there is no significant difference ($p\approx0.1$).\\
-    For each method of synergy generation alone the performance of 2 synergies is not significantly ($p>0.05$) worse. Only the over-all performance with more data is significant.
+    For each method of synergy generation alone the performance of 2 synergies is not significantly ($p>0.05$) worse. Only the over-all performance with more data becomes significant.
 \section{Classification}
     \subsection{Comparison of methods of recording}
         The different methods of recording (EEG, EMG and Low frequencies) also differ in the results. An ANOVA gives $p<0.001$ for all classifications done on 4 different movements and rest.
@@ -191,7 +191,7 @@
                 \caption{EMG predicted from EEG direct or via Autoencoder}
                 \label{fig:directViaEMG}
             \end{figure}
-        \subsubsection{Prediction via Synergies}
+        \subsubsection{Different Synergies}
             When predicting via synergies there is no significant difference between Autoencoder, PCA and NMF data ($p>0.85$).
     \subsection{EEG}
         \subsubsection{Offset}