\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} shows.\\
    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 these caps even use dry electrodes which allow for more comfort without loosing predictive strength (cf. \cite{Yeung15}). So everybody may put on and off an EEG-cap  without high costs for production or placement.

    Predicting synergies instead of positions or movement is closer to the concept the nervous system uses. This should make it easier to predict them while we can also use them to move an robotic arm or an quadrocopter.
    Because there are different possibilities to calculate synergies from EMG we compare them and try to reconstruct movement from them.

    To be able to compare the results similar calculations were done with other data and paradigms like direct prediction from EEG.
\section{Overview}
    After this Introduction in Materials and Methods (Chapter \ref{chp:mat}) we show the scientific background based on the methods used in the work. These reach from PCA and Autoencoders over 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})
