\chapter{Future Work}
    \section{Classification}
        Our results in the topic of classification are not very reliable since we did the classification based on EMG (cf. section \ref{mm:newClass}). It would be interesting to analyze data where the stimulus is matched to the EEG signal and check for early detectability (e.g. with low frequencies as \cite{Lew14}).\\
        Additionally classification - which is enough for some tasks - could be compared to regression. If there is only a limited set of movements a robotic prosthesis has to perform, it could use classification. This should give a lower error rate since the different movements can be distinguished better.
    \section{Measurement of error}
        For comparison of regression and classification it could be interesting to introduce another measure for performance than just classified correctly or not. It could be interesting how much the predicted movement differs from the real even in the classification task. In that way one would get a measure to decide whether using classification instead of regression pays off.\\
        For this analysis also a variable number of classes would be interesting since 4 movements (as in our setting) is not enough to use an artificial arm.
    \section{Offset}
        There is no significant effect of an offset in our configuration. When using smaller EEG windows however there might be one. This could be tried in further analyses with small EEG windows.\\
        These small windows however will probably bring other problems as e.g. unstable transformation into Fourier space. So maybe it is necessary to use large windows, then an offset is unnecessary.
    \section{Synergies}
