diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex index 623f1ba..0cb09b0 100644 --- a/text/thesis/03Results.tex +++ b/text/thesis/03Results.tex @@ -64,6 +64,11 @@ \subsection{Trade-off parameter} \label{res:maxC} With a cross validation we compare the results for the soft-margin parameter for $\lambda=0.1,1,10$. The results are shown in figure~\ref{fig:svmCV}.\\ + + \begin{figure} + \caption{results of crossvalidation of the Support Vector Machine} + \label{fig:svmCV} + \end{figure} TODO%TODO \subsection{Confusion Matrices} A confusion matrix shows whether there is systematic error in classification. In figure \ref{fig:cmFull} there are the confusion matrices for EEG and Low Frequency data, in figure \ref{fig:cmEMG} there is the confusion matrix for EMG data. Since EMG works well for classifying Move/Rest there is also one where only the decision is shown which movement is present. In the second plot we see that many movements are classified as class 3. Especially those belonging to class 2. @@ -178,13 +183,15 @@ \label{fig:directViaPos} \end{figure} \subsubsection{EMG} - There is a significant difference between predicting EMG from EEG directly or via Autoencoders ($p<0.001$, see figure~\ref{fig:directViaEMG}). + There is a significant difference between predicting EMG from EEG directly or via Autoencoders ($p<0.001$, see figure~\ref{fig:directViaEMG}). The prediction via Autoencoders performs a bit worse (mean is about 0.03 lower). \begin{figure} \centering \includegraphics[width=\textwidth]{pictures/results/predictEMGfromEEG.png} \caption{EMG predicted from EEG direct or via Autoencoder} \label{fig:directViaEMG} \end{figure} + \subsubsection{Prediction via Synergies} + When predicting via synergies there is no significant difference between Autoencoder, PCA and NMF data ($p) \subsection{EEG} \subsubsection{Offset} \label{res:offsetEEG} @@ -199,6 +206,14 @@ \caption{Prediction of EMG from EEG} \label{fig:EEGemg} \end{figure} + \subsubsection{Synergies} + Autoencoder data can be predicted better from EEG than EMG ($p<0.05$). PCA shows no significant difference ($p\approx0.07$). NMF data also can be predicted better ($p<0.01$).\\ + An overview is shown in figure~\ref{fig:predictEMGSyn}. + \begin{figure} + \includegraphics[width=\textwidth]{pictures/results/predictEMGSyn.png} + \caption{Predicting EMG or Synergies from EEG} + \label{fig:predictEMGSyn} + \end{figure} \subsection{EMG} Using a offset or not does not make any difference since the offset is only applied on EEG-data (cf. \ref{mat:offset}).\\ Predicting synergies from EMG does not make sense since they are computed from EMG (cf. \ref{mat:synergies}).\\ diff --git a/text/thesis/04Discussion.tex b/text/thesis/04Discussion.tex index 4d71a2e..b553806 100644 --- a/text/thesis/04Discussion.tex +++ b/text/thesis/04Discussion.tex @@ -53,14 +53,21 @@ %TODO: 2, 4 % Autoencoder better when having fewer synergies(?) \subsection{Autoencoder, PCA or NMF} - %TODO + In many applications the synergies computed with different methods perform similar, however some differences can be found. + \subsubsection{Prediction from EEG} + PCA data is predicted from EEG significantly worse than e.g. autoencoder data ($p<0.001$). Between NMF and autoencoder there is no significant difference.\\ + We conclude autoencoder and NMF are to prefer when looking for good predictability from EEG. + \subsubsection{Number of Synergies} + TODO \subsection{Prediction via Synergies} Of course the prediction via Synergies is a bit worse than direct prediction, since the machine learning techniques could do the same dimensionality reduction and also much more.\\ This decrease however is not large which suggests that synergies are a valid step in between.\\ In addition the prediction of synergies from EEG are significantly ($p<0.05$) better than the prediction of EMG. So the representation as synergies probably matches the representation in the brain better. This could mean that the controlling of a prostheses should be done via synergies - representing the representation in the brain and being easier to implement than a prosthesis listening to 32 EEG channels. \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: $0.20$). + For velocities there is no significant difference and even for positions the mean only differs about $0.03$ (EMG: $0.23$, Autoencoder: $0.20$).\\ + For the use of Synergies this is a great sign: Most of the information being present in the muscle activity can be condensed to few synergies. This strongly supports the idea of synergies.\\ + Figure \ref{fig:predictEMGSyn} shows that Synergies can be predicted better from EEG than EMG. Part of this effect may be explained by lower dimensionality however this is not the only reason since PCA is predicted similarly well as EMG. Another explanation is that Synergies represent an intermediate step between EEG and EMG. They are lacking some of the instability and noise of EMG and at the same time are more focused than the EEG signal. \section{Topographical information} \label{dis:topo} In the beta channel (see figure \ref{fig:topoBeta}) we see high activity in the right hemisphere. This is probably an artifact of muscle movements since the commands to drive the right arm should be produced in the left hemisphere.\\ diff --git a/text/thesis/05Future.tex b/text/thesis/05Future.tex index 4561c93..af15ff8 100644 --- a/text/thesis/05Future.tex +++ b/text/thesis/05Future.tex @@ -17,3 +17,6 @@ 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} TODO %TODO + \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. diff --git a/text/thesis/Acd.tex b/text/thesis/Acd.tex index 2e63d85..0113826 100644 --- a/text/thesis/Acd.tex +++ b/text/thesis/Acd.tex @@ -29,7 +29,9 @@ ] [T [O - [DO] + [D + [O]%TODO + ] ] ] ] diff --git a/text/thesis/Bfunctions.tex b/text/thesis/Bfunctions.tex index 2ecf700..da8bd01 100644 --- a/text/thesis/Bfunctions.tex +++ b/text/thesis/Bfunctions.tex @@ -98,6 +98,7 @@ \section{Plots} \subsection{\texttt{mySaveFigure.m}} \texttt{mySaveFigure.m} save a given figure with default size at given filename. + %TODO: topoplot \section{Bash Scripts} \subsection{\texttt{noOfSyn.bash}} \label{code:noSyn.bash} @@ -112,4 +113,4 @@ \subsection{\texttt{psdPlot.m}} Plots PSD with pburg and pwelch. \subsection{\texttt{pickFromStruct.m}} - Allows to pick $i$th entry of a struct (without knowing the name). + Allows to pick $i$th entry of a \matlab-struct (without knowing the name). diff --git a/text/thesis/pictures/results/overviewEEGclass.png b/text/thesis/pictures/results/overviewEEGclass.png index 9ac934f..72090a1 100644 --- a/text/thesis/pictures/results/overviewEEGclass.png +++ b/text/thesis/pictures/results/overviewEEGclass.png Binary files differ diff --git a/text/thesis/pictures/results/overviewLFclass.png b/text/thesis/pictures/results/overviewLFclass.png index 3f7f0f8..2be6353 100644 --- a/text/thesis/pictures/results/overviewLFclass.png +++ b/text/thesis/pictures/results/overviewLFclass.png Binary files differ diff --git a/text/thesis/thesis.tex b/text/thesis/thesis.tex index e0b0500..adedab2 100644 --- a/text/thesis/thesis.tex +++ b/text/thesis/thesis.tex @@ -114,7 +114,7 @@ \section*{Abstract} \addcontentsline{toc}{section}{Abstract} - +%TODO \newpage \section*{Acknowledgments} %TODO : morgen nochmal lesen @@ -226,11 +226,11 @@ \pagenumbering{arabic} \setcounter{page}{1} -%% Introduction +% Introduction \input{01Introduction} \cleardoublepage -%% Materials & Methods +% Materials & Methods \input{02MaterialsAndMethods} \cleardoublepage @@ -246,7 +246,7 @@ \input{05Future} \cleardoublepage -%%Appendix +%Appendix \appendix \input{Acd} \cleardoublepage