diff --git a/text/thesis/03Results.tex b/text/thesis/03Results.tex index c00c3f9..22967c3 100644 --- a/text/thesis/03Results.tex +++ b/text/thesis/03Results.tex @@ -1,19 +1,100 @@ \chapter{Results} \label{chp:results} -\section{Number of Synergies} -\label{res:noSyn} - To determine the number of synergies to use, all EMG data is predicted with each technique and each number of synergies from itself. The result is the plot in figure~\ref{fig:noSyn}.\\ - The plot tells that 2 and 4 synergies are good values for Autoencoders, for default nevertheless 3 synergies are used here because there are also 3 dimensions of kinematics and so it is more comparable. Three is also the most efficient number of Synergies for PCA and NNMF (cf. Section \ref{dis:noSyn}).\\ - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/noSyn.png} - \caption{Self prediction accuracy of EMG with 1 to 6 synergies. Each channel of EMG and the mean performance is shown. We see a lowering of the slope at 2 and 4 synergies for Autoencoders and at 3 synergies for PCA and NMF} - \label{fig:noSyn} - \end{figure} - When comparing the results of prediction via different number of synergies, 2 synergies perform significantly ($p<0.01$) worse than 3 and 4. Between 3 and 4 synergies there is no significant difference ($p>0.1$).\\ - For each method of synergy generation alone the performance of 2 synergies is not significantly ($p>0.05$) worse. Only the over-all performance with more data becomes significant. -\section{Classification} - \subsection{Comparison of methods of recording} +\section{EMG} + \subsection{Classification} + In figure~\ref{fig:overviewEMG} the different settings for classification based on EMG-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). + \begin{figure} + \centering + \includegraphics[width=0.9\textwidth]{pictures/results/overviewEMGclass.png} + \caption{Classification with EMG-data in different configurations: with a 1s pause (top left) and in default configuration (top right) classifying into 5 classes and in default (bottom left) and pause (bottom right) configuration classifying Move and Rest only} + \label{fig:overviewEMG} + \end{figure} + + When calculating an ANOVA on the data with and without pause we get $p<0.001$. + \subsubsection{Confusion Matrix} + A confusion matrix shows whether there is systematic error in classification.\\ + 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 which movement is present is shown. In the second plot we see that many movements are classified as class 3. Especially those belonging to class 2. + \begin{figure}[p] + \centering + \includegraphics[width=\textwidth]{pictures/results/cmEMGfull.png} + \includegraphics[width=\textwidth]{pictures/results/cmEMGmovements.png} + \caption{Confusion Matrices in default configuration for EMG showing all classes (top) and only the movement classes (bottom)} + \label{fig:cmEMG} + \end{figure} + \subsection{Regression} + Using an 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}).\\ + 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 ($p>0.1$). Predicting $\theta$ is worse significantly ($p<0.001$) for positions and velocities (also see tables \ref{tab:corrKin} and \ref{tab:corrPos}). + + There is no significant effect of the use of a pause when predicting velocities from EMG ($p>0.1$). +\section{EEG} + \subsection{Classification} + In figure~\ref{fig:overviewEEG} the different settings for classification based on EEG-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). Runs with offset have an offset of 1 or 2 (cf. \ref{mat:offset}). + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/overviewEEGclass.png} + \caption{Classification with EEG-data in different configurations: default (top left), 1s pause (top, 2nd from left), pause and offset of 1 (top, 3rd from left), offset of 1 only (top right), offset of 2 (bottom left), default but classifying Move and Rest only (bottom mid) and classifying Move and Rest with 1s Pause (bottom right)} + \label{fig:overviewEEG} + \end{figure} + \subsubsection{Confusion Matrix} + In figure \ref{fig:cmEEGFull} there is the confusion matrix for EEG. It shows a main diagonal with relatively high values, the right class is chosen more often than other classes. + \begin{figure}[p] + \centering + \includegraphics[width=\textwidth]{pictures/results/cmEEGfull.png} + \caption{Confusion Matrix in default configuration for EEG} + \label{fig:cmEEGFull} + \end{figure} + \subsection{Regression} + When predicting velocities from EEG we get mean correlations of $(0.18,0.20,0.01)$, for positions we get $(0.57,0.56,0.50)$. + \subsubsection{Offset} + \label{res:offsetEEG} + Offset makes no significant difference when predicting Synergies (Autoencoder: $p>0.1$, PCA: $p>0.1$, NMF: $p>0.1$) or velocities ($p>0.1$) or positions ($p>0.1$). + \subsubsection{Pause} + Whether there is a pause of 1s or only 0.5s doesn't make a significant difference for Autoencoder ($p>0.1$), PCA ($p>0.1$), NMF ($p>0.1$) or Velocities ($p>0.1$). + \subsubsection{EMG} + For comparison also EMG was predicted from EEG. The results are shown in figure \ref{fig:EEGemg}. There are no significant differences between the channels ($p>0.1$). + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/EEGemg.png} + \caption{Prediction of the 6 EMG-channels from 32 EEG-channels; the means of the channels are not significantly different from each other} + \label{fig:EEGemg} + \end{figure} +\section{Low Frequencies} + \subsection{Classification} + In figure~\ref{fig:overviewLF} the different settings for classification based on LowFrequency(LF)-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). Runs with offset have an offset of 1 or 2 (cf. \ref{mat:offset}). + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/overviewLFclass.png} + \caption{Classification with LF-data in different configurations: 1s pause (top left), default (top, 2nd from left), pause and offset of 1 (top, 3rd from left), offset of 1 only (top right), offset of 2 (bottom left), default but classifying Move and Rest only (bottom mid) and classifying Move and Rest with 1s Pause (bottom right)} + \label{fig:overviewLF} + \end{figure} + \subsubsection{Confusion Matrix} + In figure \ref{fig:cmLFFull} there is the confusion matrix for LF. It shows a main diagonal with relatively high values, the right class is chosen more often than other classes. However there are also quite high values for Rest as class. + \begin{figure}[p] + \centering + \includegraphics[width=\textwidth]{pictures/results/cmLFfull.png} + \caption{Confusion Matrix in default configuration for Low Frequencies} + \label{fig:cmLFFull} + \end{figure} + \subsection{Regression} + When predicting velocities from Low Frequencies we get mean correlations of $(0.04,0.07,-0.01)$, for positions we get $(0.27,0.26,0.20)$. + \subsubsection{Offset} + \label{res:offsetLF} + Offset makes no significant difference for predicting Autoencoder ($p>0.1$), PCA ($p>0.1$), NMF ($p>0.1$), velocities ($p>0.1$) or position ($p>0.1$). + \subsubsection{Pause} + There is no effect of pause for velocities from low frequencies ($p>0.1$).\\ + However there is an effect for Autoencoder ($p<0.001$), PCA ($p<0.001$) and NMF ($p<0.001$). + + The plot shows a better performance with a shorter pause and more data taken in (see figure~\ref{fig:lfToAutoencPause}) + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/lfToAutoencPause.png} + \caption{Autoencoder data predicted from Low Frequencies without (left) and with pause (right)} + \label{fig:lfToAutoencPause} + \end{figure} +\section{Comparison of methods of recording} + \subsection{Classification} The different methods of recording (EEG, EMG and Low frequencies) also differ in the results. An ANOVA gives $p<0.001$ for all classifications done on 4 different movements and rest. \begin{figure} \centering @@ -37,58 +118,7 @@ \caption{Accuracys in \% for the different methods of recording in default configuration} \label{tab:accs} \end{table} - \subsection{EMG} - In figure~\ref{fig:overviewEMG} the different settings for classification based on EMG-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). - \begin{figure} - \centering - \includegraphics[width=0.9\textwidth]{pictures/results/overviewEMGclass.png} - \caption{Classification with EMG-data in different configurations: with a 1s pause (top left) and in default configuration (top right) classifying into 5 classes and in default (bottom left) and pause (bottom right) configuration classifying Move and Rest only} - \label{fig:overviewEMG} - \end{figure} - When calculating an ANOVA on the data with and without pause we get $p<0.001$. - \subsection{EEG} - In figure~\ref{fig:overviewEEG} the different settings for classification based on EEG-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). Runs with offset have an offset of 1 or 2 (cf. \ref{mat:offset}). - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/overviewEEGclass.png} - \caption{Classification with EEG-data in different configurations: default (top left), 1s pause (top, 2nd from left), pause and offset of 1 (top, 3rd from left), offset of 1 only (top right), offset of 2 (bottom left), default but classifying Move and Rest only (bottom mid) and classifying Move and Rest with 1s Pause (bottom right)} - \label{fig:overviewEEG} - \end{figure} - \subsection{Low Frequencies} - In figure~\ref{fig:overviewLF} the different settings for classification based on LowFrequency(LF)-data are shown. Default has values as in \ref{mat:default}. The runs with pause leave out the data 1 second before the movement begins (cf. \ref{mat:pause}). Runs with offset have an offset of 1 or 2 (cf. \ref{mat:offset}). - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/overviewLFclass.png} - \caption{Classification with LF-data in different configurations: 1s pause (top left), default (top, 2nd from left), pause and offset of 1 (top, 3rd from left), offset of 1 only (top right), offset of 2 (bottom left), default but classifying Move and Rest only (bottom mid) and classifying Move and Rest with 1s Pause (bottom right)} - \label{fig:overviewLF} - \end{figure} - \subsection{Trade-off parameter} - \label{res:maxC} - With a cross validation the results for the soft-margin parameter are compared 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. - \begin{figure}[p] - \centering - \includegraphics[width=\textwidth]{pictures/results/cmEEGfull.png} - \includegraphics[width=\textwidth]{pictures/results/cmLFfull.png} - \caption{Confusion Matrices in default configuration for EEG (top) and Low Frequencies (bottom)} - \label{fig:cmFull} - \end{figure} - \begin{figure}[p] - \centering - \includegraphics[width=\textwidth]{pictures/results/cmEMGfull.png} - \includegraphics[width=\textwidth]{pictures/results/cmEMGmovements.png} - \caption{Confusion Matrices in default configuration for EMG showing all classes (top) and only the movement classes (bottom)} - \label{fig:cmEMG} - \end{figure} -\section{Regression} - \subsection{Comparison of methods of recording} + \subsection{Regression} \subsubsection{Velocities} Predicting velocities from EEG, EMG and Low Frequencies is significantly($p<0.001$) pairwise different (cf. figure~\ref{fig:corrEEGemgLF}). The corresponding $p$-Values of the ANOVA are given in table~\ref{tab:pCorr}.\\ The over all performance is given in table \ref{tab:corrKin}. @@ -165,103 +195,17 @@ \caption{Correlations for the different methods of recording in default configuration predicting positions} \label{tab:corrPos} \end{table} - \subsection{Compare Prediction direct and via Synergies} - \label{res:differentSynergiesVia} - \subsubsection{Velocities} - There is a significant($p<0.001$) difference between the predictions. The different synergies however have no significant difference ($p>0.1$). Also see figure~\ref{fig:directVia}. - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/predictKinfromEEG.png} - \caption{Correlations for movement-velocities predicted from EEG directly or via Synergies} - \label{fig:directVia} - \end{figure} - \subsubsection{Positions} - There is a significant($p<0.001$) difference between the predictions. The different synergies however have no significant difference ($p>0.1$). Also see figure~\ref{fig:directViaPos}. - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/predictPosfromEEG.png} - \caption{Correlations for positions predicted from EEG directly or via Synergies} - \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}). The prediction via Autoencoders performs a bit worse (mean $r\sim 0.03$). - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/predictEMGfromEEG.png} - \caption{Correlations for EMG predicted from EEG directly or via Autoencoder} - \label{fig:directViaEMG} - \end{figure} - \subsubsection{Different Synergies} - When predicting via synergies there is no significant difference between Autoencoder, PCA and NMF data ($p>0.1$). - \subsection{EEG} - \subsubsection{Offset} - \label{res:offsetEEG} - Offset makes no significant difference when predicting Synergies (Autoencoder: $p>0.1$, PCA: $p>0.1$, NMF: $p>0.1$) or velocities ($p>0.1$) or positions ($p>0.1$). - \subsubsection{Pause} - Whether there is a pause of 1s or only 0.5s doesn't make a significant difference for Autoencoder ($p>0.1$), PCA ($p>0.1$), NMF ($p>0.1$) or Velocities ($p>0.1$). - \subsubsection{EMG} - For comparison also EMG was predicted from EEG. The results are shown in figure \ref{fig:EEGemg}. There are no significant differences between the channels ($p>0.1$). - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/EEGemg.png} - \caption{Prediction of the 6 EMG-channels from 32 EEG-channels} - \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 ($p>0.05$). 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 (mean over channels) or Synergies (mean) 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}).\\ - 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}). - \subsubsection{Pause} - There is no significant effect of the use of a pause when predicting velocities from EMG ($p>0.1$). - \subsection{Low Frequencies} - \subsubsection{Offset} - \label{res:offsetLF} - Offset makes no significant difference for predicting Autoencoder ($p>0.1$), PCA ($p>0.1$), NMF ($p>0.1$), velocities ($p>0.1$) or position ($p>0.1$). - \subsubsection{Pause} - There is no effect of pause for velocities from low frequencies ($p>0.1$).\\ - However there is an effect for Autoencoder ($p<0.001$), PCA ($p<0.001$) and NMF ($p<0.001$). +\section{Cross-validation Parameters} + \subsection{SVM} + \label{res:maxC} + With a cross validation the results for the soft-margin parameter are compared for $\lambda=0.1,1,10$. The results are shown in figure~\ref{fig:svmCV}.\\ - The plot shows a better performance with a shorter pause and more data taken in (see figure~\ref{fig:lfToAutoencPause}) - \begin{figure} - \centering - \includegraphics[width=\textwidth]{pictures/results/lfToAutoencPause.png} - \caption{Autoencoder data predicted from Low Frequencies without (left) and with pause (right)} - \label{fig:lfToAutoencPause} - \end{figure} - \subsection{Autoencoder} - In table~\ref{tab:corrAutoenc} the correlations for velocities and positions predicted from Autoencoder are given. The data for the Autoencoder were calculated from recorded EMG data. - \begin{table} - \centering - \begin{math} - \begin{array} - {r||c|c|c|c} - &\text{velocities}&\text{positions}\\\hline - mean&(0.05,0.08,0.01)&(0.20,0.29,0.11)\\ - std&(0.04,0.05,0.02)&(0.12,0.16,0.08)\\ - max&(0.18,0.17,0.08)&(0.51,0.60,0.38)\\ - min&(-0.02,-0.01,-0.01)&(0.03,0.03,0.02) - \end{array} - \end{math} - \caption{Correlations for predicting velocities and positions from Autoencoder data} - \label{tab:corrAutoenc} - \end{table} - \subsubsection{Comparison with EMG} - When compared to the original 6D EMG data as a predictor a 3D autoencoder is only significantly worse when predicting positions ($p<0.05$), not for velocities ($p>0.1$). - \begin{figure} - \includegraphics[width=\textwidth]{pictures/results/EMGautoencPos.png} - \caption{Predicting positions from EMG (left) or Autoencoder (right)} - \label{fig:EMGautoencPos} - \end{figure} - \subsection{Cross-validation of Ridge Parameter} + \begin{figure} + \caption{results of cross-validation of the Support Vector Machine} + \label{fig:svmCV} + \end{figure} + TODO%TODO + \subsection{RIDGE-Regression} In tables \ref{tab:ridgeParamEMGkin}, \ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} we find the number of 'wins' for each parameter\footnote{\ref{tab:ridgeParamHighKin} and \ref{tab:ridgeParamAO6Kin} were calculated with a order for Burg's method of 50 instead of the later default of 250}. A 'win' refers to a run where this $\lambda$ scored the highest correlation.\\ For EMG there is no clear preference but it seems like 100 should work as parameter. For EEG we see a clear preference for $\lambda=100$. Low Frequencies seem to prefer a lower parameter about 10 however this was only evaluated for one session. For all other runs $\lambda = 100$ is used for all methods, better results might be possible with a parameter adapted better. @@ -306,10 +250,86 @@ Low ($\lambda\le 1$) values were only tested for EEG in a separate run} \label{tab:ridgeParamAO6Kin} \end{table} +\section{Synergies} + \subsection{Number of Synergies} + \label{res:noSyn} + To determine the number of synergies to use, all EMG data is predicted with each technique and each number of synergies from itself. The result is the plot in figure~\ref{fig:noSyn}.\\ + The plot tells that 2 and 4 synergies are good values for Autoencoders, for default nevertheless 3 synergies are used here because there are also 3 dimensions of kinematics and so it is more comparable. Three is also the most efficient number of Synergies for PCA and NNMF (cf. Section \ref{dis:noSyn}).\\ + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/noSyn.png} + \caption{Self prediction accuracy of EMG with 1 to 6 synergies. Each channel of EMG and the mean performance is shown. We see a lowering of the slope at 2 and 4 synergies for Autoencoders and at 3 synergies for PCA and NMF} + \label{fig:noSyn} + \end{figure} + When comparing the results of prediction via different number of synergies, 2 synergies perform significantly ($p<0.01$) worse than 3 and 4. Between 3 and 4 synergies there is no significant difference ($p>0.1$).\\ + For each method of synergy generation alone the performance of 2 synergies is not significantly ($p>0.05$) worse. Only the over-all performance with more data becomes significant. + \subsection{Autoencoder} + In table~\ref{tab:corrAutoenc} the correlations for velocities and positions predicted from Autoencoder are given. The data for the Autoencoder were calculated from recorded EMG data. + \begin{table} + \centering + \begin{math} + \begin{array} + {r||c|c|c|c} + &\text{velocities}&\text{positions}\\\hline + mean&(0.05,0.08,0.01)&(0.20,0.29,0.11)\\ + std&(0.04,0.05,0.02)&(0.12,0.16,0.08)\\ + max&(0.18,0.17,0.08)&(0.51,0.60,0.38)\\ + min&(-0.02,-0.01,-0.01)&(0.03,0.03,0.02) + \end{array} + \end{math} + \caption{Correlations for predicting velocities and positions from Autoencoder data} + \label{tab:corrAutoenc} + \end{table} + \subsubsection{Comparison with EMG} + When compared to the original 6D EMG data as a predictor a 3D autoencoder is only significantly worse when predicting positions ($p<0.05$), not for velocities ($p>0.1$). + \begin{figure} + \includegraphics[width=\textwidth]{pictures/results/EMGautoencPos.png} + \caption{Predicting positions from EMG (left) or Autoencoder (right)} + \label{fig:EMGautoencPos} + \end{figure} + \subsection{Compare Prediction direct and via Synergies} + \label{res:differentSynergiesVia} + \subsubsection{Velocities} + There is a significant ($p<0.001$) difference between the predictions. The different synergies however have no significant difference ($p>0.1$). Also see figure~\ref{fig:directVia}. + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/predictKinfromEEG.png} + \caption{Correlations for movement-velocities predicted from EEG directly or via Synergies} + \label{fig:directVia} + \end{figure} + \subsubsection{Positions} + There is a significant ($p<0.001$) difference between the predictions. The different synergies however have no significant difference ($p>0.1$). Also see figure~\ref{fig:directViaPos}. + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/predictPosfromEEG.png} + \caption{Correlations for positions predicted from EEG directly or via Synergies} + \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}). The prediction via Autoencoders performs a bit worse (mean $r\sim 0.03$). + \begin{figure} + \centering + \includegraphics[width=\textwidth]{pictures/results/predictEMGfromEEG.png} + \caption{Correlations for EMG predicted from EEG directly or via Autoencoder} + \label{fig:directViaEMG} + \end{figure} + \subsection{Different Synergies} + \subsubsection{Prediction via Synergies} + When predicting via synergies there is no significant difference between Autoencoder, PCA and NMF data ($p>0.1$). + \subsubsection{Prediction from EEG} + Autoencoder data can be predicted better from EEG than EMG from EEG ($p<0.05$). PCA shows no significant difference ($p>0.05$). 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 (mean over channels) or Synergies (mean) from EEG} + \label{fig:predictEMGSyn} + \end{figure} \section{Topographical plots} \label{res:topo} In figure \ref{fig:topoAlpha} we see the difference between move and rest in the alpha band, in \ref{fig:topoBeta} beta band (13-20Hz) is displayed.\\ Values greater 0 stand for more activity when moving, negative values mean less activity. A value of e.g. 0.15 stands for $15\%$ higher activity when moving. + + In figure \ref{fig:topoAlpha24} the difference between two reaching movements (class 2 and 4) is shown. Here positive values stand for higher activation for class 2 and vice versa. \begin{figure}[p] \centering \includegraphics[height=0.4\textheight]{pictures/results/topoAlpha.png} diff --git a/text/thesis/04Discussion.tex b/text/thesis/04Discussion.tex index dbfd508..7a3b2d1 100644 --- a/text/thesis/04Discussion.tex +++ b/text/thesis/04Discussion.tex @@ -23,7 +23,7 @@ I show that the use of low frequencies (at least as I did it here) has no advantage over the use of EMG (see table \ref{tab:pCorr}). This might also be a hint that movement artifacts 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 this configuration. - Which is interesting nevertheless is, that low frequencies also occur in rest. Quite some of the movements are classified as rest (see figure \ref{fig:cmFull}). If a sample is classified correctly as movement it is quite likely that is is also classified correctly - however with an preference on class 3 again. This matches the understanding of low frequencies as pre-movement activation mainly belonging to voluntary movement. The subjects probably plan all the possible movements while in rest to execute once the stimulus is shown. + Which is interesting nevertheless is, that low frequencies also occur in rest. Quite some of the movements are classified as rest (see figure \ref{fig:cmLFFull}). If a sample is classified correctly as movement it is quite likely that is is also classified correctly - however with an preference on class 3 again. This matches the understanding of low frequencies as pre-movement activation mainly belonging to voluntary movement. The subjects probably plan all the possible movements while in rest to execute once the stimulus is shown. \section{Velocities and Positions} \label{dis:velPos} I expected better performance when predicting velocities instead of absolute positions. The findings however show the opposite. The performance is quite a lot better when predicting positions directly.\\ diff --git a/text/thesis/05Future.tex b/text/thesis/05Future.tex index c09a712..b506d68 100644 --- a/text/thesis/05Future.tex +++ b/text/thesis/05Future.tex @@ -19,7 +19,7 @@ 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} - I showed 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.\\ + 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.