\chapter{Introduction}
\section{Motivation}
%TODO: More explanation, maybe better link to the topic
    \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 thought. This would lower the reaction time needed to activate the breaks for example.

    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 (e.g. for surgery).

    Predicting synergies instead of positions or movement is nearer to the concept the nervous system uses. This should make them easier to predict while we can also use them to move an robotic arm or an quadrocopter.
\section{Scientific background}
    \subsection{BCIs}
        The idea of BCIs began to spread in the 1970s when Vidal published his paper (\cite{Vidal73}).\\
        First approaches used invasive BCIs earlier in Animals (rodents and monkeys) later also in humans. Invasive BCIs in humans were mostly implanted when the human was under brain surgery for another reason like epilepsy. Problems of invasive BCIs are the need to cut through skull and dura mater. This can lead to infections and severe brain damage.\\
        An improvement were less invasive BCIs with e.g. ECoG which is placed below the skull but outside the dura which decreased the risk for infections massively.\\
        Measuring outside the skull entails even less risk, the dura and skull however lower the quality of the signal massively. With some improvements EEG has a spatial resolution of 2-3 cm (cf. \cite{Babiloni01}). This is quite bad compared to the single neuron one can observe with invasive methods. However we are more interested in the activity of areas then single cells for our task, so EEG meets our requirements here.
    \subsection{EEG}
        When using EEG one measures the electrical fields on the scalp that are generated by activity of neurons in the brain. These measurements allow some interpretation about what is happening inside the skull. In our application we use the recorded currents directly to train a SVM or as predictor for regression.\\
        The frequencies typically used for movement prediction in EEG are about 8-24 Hz (\cite{Blokland15},\cite{Ahmadian13},\cite{Wang09}).
    \subsection{Low Frequencies}
        Another approach is looking at the low frequency features (below 1Hz) in the signal. %TODO citing
    \subsection{Support Vector Machines (SVM)}
    \subsection{Synergies}

