\chapter{Discussion}
\section{Number of Synergies}
\label{dis:noSyn}
    As shown in section~\ref{res:noSyn} 2 and 4 Synergies are good values for Autoencoder since the slope of the mean prediction is steeper before than after. Another neuron doesn't improve the result as much as the last.\\
    For PCA and NNMF this value is reached at 3 as figure \ref{fig:noSyn} shows.
    %TODO: 2, 4
    % Autoencoder better when having fewer synergies(?)
\section{EMG}
\label{dis:emg}
    Predictions of velocities and positions are quite bad from EMG.
    The prediction of the $y$-dimension is a bit better than $x$ ($p<0.05$) for velocities. For positions there is no significant difference ($p\approx 0.31$). Predicting $\theta$ is worse significantly ($p<0.001$) for positions and velocities (also see tables \ref{tab:corrKin} and \ref{tab:corrPos}). %Results?

    A correlation about $0.2$ cannot be used for a BCI. There might be some way of improving the predictions and it might be done several times to higher the correlations, finding another approach however is more promising.\\
    In addition when a BCI is needed often the are no EMG signals since the muscles do not work as they are told (e.g. after stroke) and so do not generate the corresponding activity.

    Out of these reasons we only use EMG as benchmark for other approaches: If the muscles would work this correlation could be reached.
\section{EEG}
\label{dis:eeg}
    Predictions from EEG to velocities and position are significantly better than those from EMG (see tables \ref{tab:pCorr},~\ref{tab:corrKin},~\ref{tab:pCorrPos} and \ref{tab:corrPos}).\\
    This might be because %TODO: reason

    When predicting velocities or positions from EEG there is no significant difference between $x$ and $y$. The difference between $x$ and $y$ and the angle $\theta$ is larger for velocities than for absolute positions since the absolute angle prediction a lot better than the prediction of change.\\
    This again is an indication that the actual position is more important for the activity in brain than the change of position as itself.
\section{Low Frequencies}
\label{dis:lf}
    Our findings concerning low frequencies are a lot less promising than e.g. in \cite{Lew14}.\\
    The reason for that might be that the movements were not self induced but extrinsically motivated by a cue. \citeauthor{Lew14} however use low frequencies exactly for the purpose to detect voluntary movement.\\
    We show that the use of low frequencies (at least as we did it here) has no advantage over the use of EMG (see table \ref{tab:pCorr}). This might also be a hint that movement relics were have the biggest part in low frequencies while moving. This however makes it impossible to use them for continuous tasks.\\
    Low frequencies are great to early detect voluntary movement but are not applicable in our configuration.
\section{Velocities and Positions}
\label{dis:velPos}
    We expected better performance when predicting velocities instead of absolute positions. Our findings however show the opposite. The performance is quite a lot better when predicting positions directly.\\
    This might mean that both EMG and EEG data carry more information about the actual configuration and not only about the change.
    %TODO: reason?
\section{Pause}
\label{dis:pause}
    The use of a 1 second pause before movement onset only shows significant differences for classification of EMG and predicting Synergies from low frequencies.
    \subsection{Classification from EMG}
        When doing classification from EMG data there is an great improvement when leaving the whole second before movement onset out. This results from the way we did the classification. Since we decided on when the movement starts based on the EMG data.\\
        What this finding shows is that the threshold is chosen well to classify the movements.
    \subsection{Synergies from Low Frequencies}
        There is a significant improvement for predicting Synergies from low frequency data. This shows again what \cite{Lew14} proposed; in low frequency features we find mainly pre-movement activation. Activity while moving is probably mostly occluded by motor commands.
\section{Offset}
\label{dis:offset}
    Applying an offset when using EEG-data or not does not make a significant difference. This is probably due to our configuration with EEG windows as large as 1 second. If smaller windows were used an offset could help, here there is no difference.
\section{Synergies}
\label{dis:synergies}
    \subsection{Autoencoder}
    \subsection{Principal Component Analysis}
    \subsection{Non-negative Matrix Factorization}
    \subsection{Prediction via Synergies}

    \subsection{Comparison with EMG}
        The results show that the dimensionality reduction from 6 dimensional EMG to 3 dimensional Synergies (here via autoencoder) does not cost much information when predicting velocities and positions.\\
        For velocities there is no significant difference and even for positions the mean only differs about $0.03$ (EMG: $0.23$, Autoencoder: $0.20$).
