\chapter{Future Work}
\label{chp:fut}
\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 if it is necessary to use large windows, an offset is unnecessary.
\section{Use of EEG channels}
    To achieve higher performance it would be interesting to identify those EEG channels that contribute most in an good estimation of arm movements or position. There should be channels that do not carry much information for those differentiations, this however has to be explored better.\\
    In this context research could also be done to find out which frequencies allow for the best predictions.
\section{Self-chosen movement}
    For a better use of low frequency features our work could be compared with data recorded when subjects move voluntarily. This might also influence the way synergies are predicted and could lead to an better prediction.\\
    Additionally this task matches the requirements for an BCI better, as movement in daily life is more voluntary than decided by a single auditory cue.
\section{Synergies}
    \subsection{Generation of Synergies}
        We proofed the plausibility of synergies here so the next step could be to improve the acquisition. Generating them from EMG may include unnecessary information. The generation of synergies as an intermediate step between EEG (or generally brain activity) and EMG (or generally muscle activity) my achieve even better results.\\
        A dimensionality reduction in EEG only probably will not work since there is to much unrelated activity, EMG only bears the problem of lower fit to the movement as we showed.
    \subsection{Autoencoders}
        We did not find significantly better performance of autoencoders even with only 2 synergies. Since this was not the focus of our work here that might however be possible. Additional research is needed to answer finally which method is best to generate synergies.
