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\title{Movement Prediction from EEG based on Synergies}
\author{Jan-Peter Hohloch}
\renewcommand{\title}{Movement Prediction from EEG\\ Based on Synergies}
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\begin{titlepage}
 \begin{center}
  {\LARGE Eberhard Karls Universität Tübingen}\\
  {\large Mathematisch-Naturwissenschaftliche Fakultät \\
Wilhelm-Schickard-Institut für Informatik\\[4cm]}
  {\huge Master's Thesis Informatics\\[2cm]}
  {\Large\bf \title\\}
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 {\large \author }\\[0.5cm]
\today\\[4cm]
{\small\bf Reviewers}\\[0.5cm]
  \parbox{7cm}{\begin{center}{\large Prof. Dr. Wolfgang Rosenstiel}\\
   (Informatik)\\
  {\footnotesize Wilhelm-Schickard-Institut für Informatik\\
	Universität Tübingen}\end{center}}\hfill\parbox{7cm}{\begin{center}
  {\large Prof. Dr. Jan Benda}\\
  (Biologie)\\
  {\footnotesize Institut für Neurobiologie\\
	Universität Tübingen}\end{center}
 }
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\textbf{Hohloch, Jan-Peter:}\\
\emph{\title}\\ Master Thesis Informatics\\
Eberhard Karls Universität Tübingen\\
Thesis period: June-Nov 2016
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\section*{Abstract}
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Synergies are patterns of muscle activation where muscles are used in a coordinated way, so that not each muscle has to be activated separately. These patterns can be found in the brain and its activation.\\
This thesis shows the plausibility of synergies as an intermediate step between the brain and the muscles. The results show only a small decrease in predicting performance for position and velocity compared to the Electromyography (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 Electroencephalography (EEG) in a classification and regression context. Over all synergies perform not much worse than EMG and are predicted better from EEG.\\
Also comparison of different methods for the acquisition of synergies is done. The 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.

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\section*{Acknowledgments}
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\noindent First of all I would like to thank my supervisor Martin Spüler who always had a friendly ear for problems and gave lots of useful input.

\noindent My thanks also go to Farid Shiman, Nerea Irastorza-Landa and Andrea Sarasola-Sanz whose data I was allowed to use for my work.

\noindent Also my reviewers Professores Rosenstiel and Benda shall be named here. I want to express my thanks for their willingness to review my thesis and my hope that this thesis may found a link between their chairs for further projects.

\noindent This work would not have been possible without the Free Software Community providing \LaTeX, TikZ and Ubuntu for example. My deep gratitude goes to all the developers spending time for the improvement of commonly available software.

\noindent Last and most basic my thanks to my family who always supported me and my studies.



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\begin{tabbing}
\textbf{EEG}\hspace{2cm}\=Electroencephalography\\
\textbf{EMG}\>Electromyography\\
\textbf{fMRI}\>functional Magnetic Resonance Imaging\\
\textbf{LF}\>Low Frequency\\
\textbf{BCI}\> Brain-Computer-Interface \\
\textbf{SVM}\> Support-Vector-Machine \\
\textbf{ECoG}\> Electrocorticography \\
\textbf{PCA}\> Principal Component Analysis \\
\textbf{NMF}\> non-Negative Matrix Factorization \\
\textbf{ANN}\> Artificial Neural Network \\
\textbf{PSD}\> Power Spectral Density \\
\textbf{ALS}\> Alternating Least Squares \\
\textbf{ANOVA} \> Analysis of Variance\\
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\section*{Selbständigkeitserklärung}

Hiermit versichere ich, dass ich die vorliegende Masterarbeit
selbständig und nur mit den angegebenen Hilfsmitteln angefertigt habe und dass alle Stellen, die dem Wortlaut oder dem
Sinne nach anderen Werken entnommen sind, durch Angaben von Quellen als
Entlehnung kenntlich gemacht worden sind.
Diese Masterarbeit wurde in gleicher oder ähnlicher Form in keinem anderen
Studiengang als Prüfungsleistung vorgelegt.

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Tübingen, \today	\hfill \author \hfill
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