\chapter{Introduction}
\label{introduction}
\section{Motivation}
\label{intro:motivation}
    \qq{Reading the mind} is something humanity is and always was exited about. Whatever one may think about the possibility of doing so as a human, computers have a chance to catch a glimpse of the (neuronal) activity in the human brain and interpret it.\\
    Here we use Electroencephalography (EEG) to record brain activity and try to predict arm movements from the data.\\
    Using this as a Brain-Computer-Interface (BCI) holds the possibility of restoring e.g. a lost arm. This arm could be used as before by commands constructed in the brain. In a perfect application there would be no need of relearning the usage. The lost arm could just be replaced.\\
    Another opportunity this technique provides is support of retraining the usage of the natural arm after stroke. If it is possible to interpret the brainwaves the arm can be moved passively according to the commands formed in brain. This congruency can restore the bodies own ability to move the arm as \cite{Gomez11} show.\\
    In a slightly different context it might become possible to handle a machine (e.g. an industrial robot or mobile robots like quadrocopters) with \qq{thoughts} (i.e. brain activity) like an additional limb. One could learn to use the possibilities of the robot like the possibilities of his arm and hand to modulate something.\\
    Similar to that application it could be possible to drive a car by brain activity. This would lower the reaction time needed to activate the breaks for example by direct interaction instead of using the nerves down to the leg to press the break.

    Using non-invasive methods like EEG makes it harder to get a good signal and determine its origin. However it lowers the risk of injuries and infections which makes it the method of choice for wide spread application (cf. \cite{Collinger13}). Modern versions of EEG-caps even use dry electrodes which allow for more comfort with similar predictive strength in context of movement of the whole body due to mathematical post-processing (cf. \cite{Yeung15}). So everybody may put on and off an EEG-cap without high costs for production or placement.\\
    With EEG brainwaves can be captured that let us predict intended movements. This movement predictions however bears some problems up to now.

    Predicting synergies instead of predicting positions or movement directly may solve some of these problems, since it is closer to the concept the nervous system uses. Most likely in brain there are no neurons for every single muscle involved in movement. Instead there are synergies activated, meaning there is coordinated co-activation of different muscles. When using synergies only some basic movements have to be represented in brain and can be combined for more complex movements.\\
    Assuming this it should be easier to predict synergies while we can also use them to move a robotic arm or a quadrocopter.

    This improvements shall be shown in this thesis. To do so different methods of the acquisition of synergies from EMG are compared with other data and paradigms like direct prediction from EEG, EMG and low frequencies.
\section{Overview}%TODO
    After this Introduction in Materials and Methods (Chapter \ref{chp:mat}) we show the scientific background of the methods used in the work. These reach from Principal Component Analysis (PCA) and Autoencoders over Support Vector MAchines (SVMs) and regression to boxplots and topographical plots.\\
    In chapter \ref{chp:results} Results we show the numerical findings of our work separated into parts on synergies, classification, regression and a topographical analysis of the brain activity.\\
    This results and their meaning will be discussed in chapter \ref{chp:dis} Discussion.\\
    Finally we take a look in the possible future and discuss which further research could be done based on or related to our work (chapter \ref{chp:fut}).

    The appendix then contains a list of contents on the CD and in the repository (Appendix \ref{app:cd}) and a small documentation of the code used (Appendix \ref{app:docu})