diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex
index dd3a4d3..6612b2b 100644
--- a/text/thesis/03Results.tex
+++ b/text/thesis/03Results.tex
@@ -259,9 +259,10 @@
                 \caption{Predicting positions from EMG or Autoencoder}
                 \label{fig:EMGautoencPos}
             \end{figure}
-    \subsection{Cross-validation of Ridge Parameter}
-        TODO\\%TODO
-        For EMG we find no clear best parameter. When predicting velocities we get best parameters chosen as shown in table \ref{tab:ridgeParamEMGkin}. A 'win' refers to a run where this $\lambda$ scored the highest correlation.
+    \subsection{Cross-validation of Ridge Parameter}%TODO
+        In tables \ref{tab:ridgeParamEMGkin}, \ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} we find the number of 'wins' for each parameter\footnote{\ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} were calculated with a order for Burg's method of 50 instead of the later default of 250}. A 'win' refers to a run where this $\lambda$ scored the highest correlation.\\
+        For EMG there is no clear preference but it seems like 100 should work as parameter. For EEG we see a clear preference for $\lambda=100$. Low Frequencies seem to prefer a lower parameter about 10 however this was only evaluated for one session. %TODO, if better results
+        For all other runs we used $\lambda = 100$ for all methods, better results might be possible with a parameter adapted better.
         \begin{table}
             \centering
             \begin{math}
@@ -274,6 +275,34 @@
             \caption{Number of 'wins' for each parameter when doing ridge regression to predict velocities from EMG}
             \label{tab:ridgeParamEMGkin}
         \end{table}
+        \begin{table}
+            \centering
+            \begin{math}
+                \begin{array}
+                    {r||c|c|c|c}
+                    \lambda&100 & 1000 & 10000 & 100000\\\hline
+                    EEG & 1334 & 192 & 4 &0\\
+                    EMG & 794 & 468 & 159 & 109\\
+                    LF& 1396 & 71 & 24 & 39
+                \end{array}
+            \end{math}
+            \caption{Number of 'wins' for each parameter when doing ridge regression to predict velocities from EEG, EMG or LF}
+            \label{tab:ridgeParamHighKin}
+        \end{table}
+        \begin{table}
+            \centering
+            \begin{math}
+                \begin{array}
+                    {r||c|c|c|c||c|c|c|c}
+                    \lambda&0.001 & 0.01 & 0.1 & 1 & 1 & 5 & 10 & 100\\\hline
+                    EEG & 0&0&0& 30 & 0 & 0 & 1 & 29\\
+                    EMG & & & & & 7 & 9 & 10 & 4\\
+                    LF& & & & & 1 & 13 & 14 & 2
+                \end{array}
+            \end{math}
+            \caption{Number of 'wins' for each parameter when doing ridge regression to predict velocities from EEG, EMG or LF (run on AO6 only)}
+            \label{tab:ridgeParamAO6Kin}
+        \end{table}
 \section{Topographical plots}
     \label{res:topo}
     In figure \ref{fig:topoAlpha} we see the difference between move and rest in the alpha band, in \ref{fig:topoBeta} beta band (13-20Hz) is displayed.\\