diff --git a/text/TODO.txt b/text/TODO.txt index a596771..e40389a 100644 --- a/text/TODO.txt +++ b/text/TODO.txt @@ -118,7 +118,8 @@ 2016-11-15 ---------- * really blinking? (p.27) - +* why is EEG better than EMG? +* why are positions better than velocities? TODO diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex index 8800d8b..8627811 100644 --- a/text/thesis/02MaterialsAndMethods.tex +++ b/text/thesis/02MaterialsAndMethods.tex @@ -238,7 +238,7 @@ \begin{figure} \centering \includegraphics{experimentalDesign.jpg} - \caption{Center-out reaching task with four color-coded targets \cite{Shiman15}} + \caption{Center-out reaching task with four color-coded targets (picture from \citeauthor{Shiman15})} \label{fig:experimentalDesign} \end{figure} Of the kinematic information tracked we only used position ($x,y$) and angle ($\theta$, rotation around $z$-axis) of the hand.\\ diff --git a/text/thesis/04Discussion.tex b/text/thesis/04Discussion.tex index 5125614..dde1106 100644 --- a/text/thesis/04Discussion.tex +++ b/text/thesis/04Discussion.tex @@ -1,5 +1,46 @@ \chapter{Discussion} - \section{Number of Synergies} - \label{dis:noSyn} - As shown in section~\ref{res:noSyn} 2 and 4 Synergies are good values for Autoencoder since the slope of the mean prediction is steeper before than after. Another neuron doesn't improve the result as much as the last.\\ - For PCA and NNMF this value is reached at 3 as \ref{fig:noSyn} shows. +\section{Number of Synergies} +\label{dis:noSyn} + As shown in section~\ref{res:noSyn} 2 and 4 Synergies are good values for Autoencoder since the slope of the mean prediction is steeper before than after. Another neuron doesn't improve the result as much as the last.\\ + For PCA and NNMF this value is reached at 3 as figure \ref{fig:noSyn} shows. + %TODO: 2, 4 + % Autoencoder better when having fewer synergies(?) +\section{EMG} +\label{dis:emg} + 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}). %Results? + + A correlation about $0.2$ cannot be used for a BCI. There might be some way of improving the predictions and it might be done several times to higher the correlations, finding another approach however is more promising.\\ + In addition when a BCI is needed often the are no EMG signals since the muscles do not work as they are told (e.g. after stroke) and so do not generate the corresponding activity. + + Out of these reasons we only use EMG as benchmark for other approaches: If the muscles would work this correlation could be reached. +\section{EEG} +\label{dis:eeg} + Predictions from EEG to velocities and position are significantly better than those from EMG (see tables \ref{tab:pCorr},~\ref{tab:corrKin},~\ref{tab:pCorrPos} and \ref{tab:corrPos}).\\ + This might be because %TODO: reason + + When predicting velocities or positions from EEG there is no significant difference between $x$ and $y$. The difference between $x$ and $y$ and the angle $\theta$ is larger for velocities than for absolute positions since the absolute angle prediction a lot better than the prediction of change.\\ + This again is an indication that the actual position is more important for the activity in brain than the change of position as itself. +\section{Low Frequencies} +\label{dis:lf} + Our findings concerning low frequencies are a lot less promising than e.g. in \cite{Lew14}.\\ + The reason for that might be that the movements were not self induced but extrinsically motivated by a cue. \citeauthor{Lew14} however use low frequencies exactly for the purpose to detect voluntary movement.\\ + We show that the use of low frequencies (at least as we did it here) has no advantage over the use of EMG (see table \ref{tab:pCorr}). This might also be a hint that movement relics were 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 our configuration. +\section{Velocities and Positions} +\label{dis:velPos} + We expected better performance when predicting velocities instead of absolute positions. Our findings however show the opposite. The performance is quite a lot better when predicting positions directly.\\ + This might mean that both EMG and EEG data carry more information about the actual configuration and less about the mere change. + %TODO: reason? +\section{Pause} +\label{dis:pause} + +\section{Offset} +\label{dis:offset} + +\section{Synergies} +\label{dis:synergies} + \subsection{Autoencoder} + \subsection{Principal Component Analysis} + \subsection{Non-negative Matrix Factorization} + \subsection{Prediction via Synergies} diff --git a/text/thesis/thesis.tex b/text/thesis/thesis.tex index 4cbb9ff..b27bdf7 100644 --- a/text/thesis/thesis.tex +++ b/text/thesis/thesis.tex @@ -8,6 +8,7 @@ \documentclass[twoside,12pt,a4paper]{book} %\usepackage{reportpage} +\usepackage{natbib} \usepackage{epsf} \usepackage{graphics, graphicx} \usepackage{latexsym} @@ -253,7 +254,7 @@ \nocite{*} -\bibliographystyle{alpha} +\bibliographystyle{plainnat} \bibliography{mylit} %% Obige Anweisung legt fest, dass BibTeX-Datei `mylit.bib' verwendet %% wird. Hier koennen mehrere Dateinamen mit Kommata getrennt aufgelistet diff --git a/usedMcode/evaluationCorrelations.m b/usedMcode/evaluationCorrelations.m index 0899d38..7f04870 100644 --- a/usedMcode/evaluationCorrelations.m +++ b/usedMcode/evaluationCorrelations.m @@ -2,6 +2,12 @@ figureSavePath='/home/jph/Uni/masterarbeit/text/thesis/pictures/results/'; % mySaveFigure(gcf,strcat(figureSavePath,'plot')) +%% inspect methods of recoding +input=correlations.EEG.pos.Default; +anova1(input,{'x','y','theta'}) +anova1(input(:,1:2),{'x','y'}) +anova1([input(:,1),input(:,3)],{'x','theta'}) + %% compare methods of recording % velocities eegCorrKin=pickFromStruct(correlations.EEG.kin,1:3);