diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex
index 295079c..8fb7408 100644
--- a/text/thesis/02MaterialsAndMethods.tex
+++ b/text/thesis/02MaterialsAndMethods.tex
@@ -90,7 +90,7 @@
         The PCA of data can be done in different ways. One is calculating the eigenvectors of the covariance matrix. The principal component is the eigenvector with the highest eigenvalue. Other eigenvectors follow ordered by their eigenvalues.\\
         \begin{figure}
             \centering
-            \includegraphics[width=0.7\textwidth]{GaussianScatterPCA.jpg}
+            \includegraphics[width=0.7\textwidth]{pictures/GaussianScatterPCA.jpg}
             \caption{Gaussian Scatter with both eigenvectors, the principal component (long arrow) explaining most, the other least variance}
             \label{fig:pca}
         \end{figure}
@@ -128,7 +128,7 @@
         An autoencoder's input layer has as many neurons as there are input dimensions (e.g. one for each EMG channel). The number of hidden layer neurons may be varied, here usually 3 are used. The output layer is of the same size as the input layer. This autoencoder is shown in Figure~\ref{fig:autoenc}.
         \begin{figure}
             \centering
-            \input{autoencoder.tikz}
+            \input{pictures/autoencoder.tikz}
             \caption{Autoencoder with 6 input and 6 output neurons and a hidden layer of size 3}
             \label{fig:autoenc}
         \end{figure}
@@ -256,7 +256,7 @@
     surface EMG and 7 DOF kinematics were tracked.
     \begin{figure}
         \centering
-        \includegraphics{experimentalDesign.jpg}
+        \includegraphics{pictures/experimentalDesign.jpg}
         \caption{Center-out reaching task with four color-coded targets (picture from \cite{Shiman15})}
         \label{fig:experimentalDesign}
     \end{figure}
diff --git a/text/thesis/GaussianScatterPCA.jpg b/text/thesis/GaussianScatterPCA.jpg
deleted file mode 100644
index d0455eb..0000000
--- a/text/thesis/GaussianScatterPCA.jpg
+++ /dev/null
Binary files differ
diff --git a/text/thesis/autoencoder.tikz b/text/thesis/autoencoder.tikz
deleted file mode 100644
index 9bb422b..0000000
--- a/text/thesis/autoencoder.tikz
+++ /dev/null
@@ -1,29 +0,0 @@
-\begin{tikzpicture}[->,auto,x=1.5cm, y=1.5cm]
-
-\foreach \i in {1,2,3,4,5,6}
-  \node [state] (input-\i) at (0,2.5-\i) {};
-
-\foreach \i in {1,2,3}
-  \node [state] (hidden-\i) at (3,2-\i*1.25) {};
-
-\foreach \i in {1,2,3,4,5,6}
-  \node [state] (output-\i) at (6,2.5-\i) {};
-
-\foreach \i in {1,2,3}
-  \foreach \j in {1,2,3,4,5,6}
-    \draw  (hidden-\i) -- (output-\j);
-
-\foreach \i in {1,2,3,4,5,6}
-  \foreach \j in {1,2,3}
-    \draw (input-\i) -- (hidden-\j);
-
-
-\foreach \i in {1,2,3,4,5,6}
-  \draw [<-] (input-\i) -- ++(-1,0)
-    node [above, midway] {Ch \i};
-
-\foreach \i in {1,2,3,4,5,6}
-  \draw [->] (output-\i) -- ++(1,0)
-    node [above, midway] {Ch \i};
-
-\end{tikzpicture}
diff --git a/text/thesis/experimentalDesign.jpg b/text/thesis/experimentalDesign.jpg
deleted file mode 100644
index a2f0470..0000000
--- a/text/thesis/experimentalDesign.jpg
+++ /dev/null
Binary files differ
diff --git a/text/thesis/pca.py b/text/thesis/pca.py
deleted file mode 100644
index 7ab0948..0000000
--- a/text/thesis/pca.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import rcParams
-
-rcParams['font.family'] = 'serif'
-rcParams['font.size'] = 16
-
-
-ang = np.pi/6
-mean = np.array([1, 3])
-cov = np.array([[9, 0],
-                [0, 1]])
-rot = np.array([[np.cos(ang), -np.sin(ang)],
-                [np.sin(ang), np.cos(ang)]])
-cov = np.dot(rot, np.dot(cov, rot.T))
-
-np.random.seed(seed=1)
-data = np.random.multivariate_normal(mean, cov, size=(5000))
-x = data[:, 0]
-y = data[:, 1]
-
-plt.figure(figsize=(8, 8))
-plt.scatter(x, y, c="k", alpha=0.5, lw=0)
-plt.arrow(1, 3, 3*np.cos(ang), 3*np.sin(ang), width=0.02, color="r", lw=2,
-          overhang=0.1)
-plt.arrow(1, 3, -np.sin(ang), np.cos(ang), width=0.02, color="r", lw=2,
-          overhang=0.1)
-plt.grid(True)
-plt.axis("image")
-plt.axis([-8, 10, -6, 12])
-plt.savefig("GaussianScatterPCA.jpg")
-plt.show()
diff --git a/text/thesis/pictures/GaussianScatterPCA.jpg b/text/thesis/pictures/GaussianScatterPCA.jpg
new file mode 100644
index 0000000..10bed8b
--- /dev/null
+++ b/text/thesis/pictures/GaussianScatterPCA.jpg
Binary files differ
diff --git a/text/thesis/pictures/autoencoder.tikz b/text/thesis/pictures/autoencoder.tikz
new file mode 100644
index 0000000..9bb422b
--- /dev/null
+++ b/text/thesis/pictures/autoencoder.tikz
@@ -0,0 +1,29 @@
+\begin{tikzpicture}[->,auto,x=1.5cm, y=1.5cm]
+
+\foreach \i in {1,2,3,4,5,6}
+  \node [state] (input-\i) at (0,2.5-\i) {};
+
+\foreach \i in {1,2,3}
+  \node [state] (hidden-\i) at (3,2-\i*1.25) {};
+
+\foreach \i in {1,2,3,4,5,6}
+  \node [state] (output-\i) at (6,2.5-\i) {};
+
+\foreach \i in {1,2,3}
+  \foreach \j in {1,2,3,4,5,6}
+    \draw  (hidden-\i) -- (output-\j);
+
+\foreach \i in {1,2,3,4,5,6}
+  \foreach \j in {1,2,3}
+    \draw (input-\i) -- (hidden-\j);
+
+
+\foreach \i in {1,2,3,4,5,6}
+  \draw [<-] (input-\i) -- ++(-1,0)
+    node [above, midway] {Ch \i};
+
+\foreach \i in {1,2,3,4,5,6}
+  \draw [->] (output-\i) -- ++(1,0)
+    node [above, midway] {Ch \i};
+
+\end{tikzpicture}
diff --git a/text/thesis/pictures/experimentalDesign.jpg b/text/thesis/pictures/experimentalDesign.jpg
new file mode 100644
index 0000000..a2f0470
--- /dev/null
+++ b/text/thesis/pictures/experimentalDesign.jpg
Binary files differ
diff --git a/text/thesis/pictures/pca.py b/text/thesis/pictures/pca.py
new file mode 100755
index 0000000..6d74cee
--- /dev/null
+++ b/text/thesis/pictures/pca.py
@@ -0,0 +1,34 @@
+#!/usr/bin/python
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+
+rcParams['font.family'] = 'serif'
+rcParams['font.size'] = 16
+
+
+ang = np.pi/6
+mean = np.array([1, 3])
+cov = np.array([[9, 0],
+                [0, 1]])
+rot = np.array([[np.cos(ang), -np.sin(ang)],
+                [np.sin(ang), np.cos(ang)]])
+cov = np.dot(rot, np.dot(cov, rot.T))
+
+np.random.seed(seed=1)
+data = np.random.multivariate_normal(mean, cov, size=(5000))
+x = data[:, 0]
+y = data[:, 1]
+
+plt.figure(figsize=(8, 8))
+plt.scatter(x, y, c="k", alpha=0.5, lw=0)
+plt.arrow(1, 3, 3*np.cos(ang), 3*np.sin(ang), width=0.02, color="w", lw=2,
+          overhang=0.1)
+plt.arrow(1, 3, -np.sin(ang), np.cos(ang), width=0.02, color="w", lw=2,
+          overhang=0.1)
+plt.grid(True)
+plt.axis("image")
+plt.axis([-8, 10, -6, 12])
+plt.savefig("GaussianScatterPCA.jpg")
+plt.show()