\section{Future Work}
\label{chp:fut}
\subsection{Classification}
    My results in the topic of classification are not very reliable since I 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.
\subsection{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 having 4 movements (as in this setting) is not enough to use an artificial arm.
\subsection{Offset}
    There is no significant effect of an offset in my 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.
\subsection{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.

    My findings predict a better performance for the alpha band and occipital and parietal regions. A more detailed work on this specific topic however is necessary to decide based on more data.
\subsection{Self-chosen movement}
    For a better use of low frequency features my work could be re-done 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.
\subsection{Synergies}
    \subsubsection{Generation of Synergies}
        This thesis shows the plausibility of synergies 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) may 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 is shown above.\\
        An idea could be to try a dimensionality reduction on EEG of parts of the brain known to be involved in arm movement. This however is a far less general approach than the methods I used.\\
        A more general approach would be a neural network trained to predict EMG from EEG. The hidden layer of this network again could be used as synergies.
    \subsubsection{Autoencoders}
        I did not find significantly better performance of autoencoders even with only 2 synergies. Since this was not the focus of the work here that might however be possible. Additional research is needed to answer which method is best to generate synergies.