diff --git a/text/TODO.txt b/text/TODO.txt index 9d5e2b6..38b25f0 100644 --- a/text/TODO.txt +++ b/text/TODO.txt @@ -79,3 +79,11 @@ Aus EEG (über Synergien) kinematik Intro nur Motivation, Rest in II + + +pburg, butterworth - wie ausführlich? +EMG - usage in science +Bilder aus Paper zu Daten verwenden? +experimental design + - more data needed (sex, age, ...); slight differences in the experimental design of Shiman and Sarasola paper + - what is the angle tracked? diff --git a/text/ideas.txt b/text/ideas.txt index 6cfe922..4e44e97 100644 --- a/text/ideas.txt +++ b/text/ideas.txt @@ -1,3 +1,5 @@ explanation for bad position/movement estimation: * not known whether up or down -> gravitational influence * better to estimate EMG since this corresponds to muscle activity +explanation for bad LF +* no voluntary movement? diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex index c14073f..543381d 100644 --- a/text/thesis/02MaterialsAndMethods.tex +++ b/text/thesis/02MaterialsAndMethods.tex @@ -36,13 +36,20 @@ \subsubsection{Burg's method} \label{mat:burg} Burg's method (\cite{Burg75}) is a special case of parametric PSD estimation. It interprets the Yule-Walker-Equations as least squares problem and iteratively estimates solutions.\\ - According to \cite{Huang14} Burg's method fits well in cases with the need of high resolution. %TODO + According to \cite{Huang14} Burg's method fits well in cases with the need of high resolution. %TODO? \subsection{Low Frequencies} - Another approach is looking at the low frequency features (below 2Hz) in the signal. This was done by Liu et al. (\cite{Liu11}) and Antelis et al. (\cite{Antelis13}) for example.\\ - Antelis et al. found correlations between hand movement and low frequency signal of $(0.29,0.15,0.37)$ in the dimensions respectively. + In the 2000s there began a movement using new techniques to record ultrafast and infraslow brainwaves (above 50Hz and below 1Hz). These were found to have some importance (cf. \cite{Vanhatalo04}).\\ + Also in predicting movements there was found some significance in low frequency as was done by Liu et al. (\cite{Liu11}) and Antelis et al. (\cite{Antelis13}) for example. Antelis et al. found correlations between hand movement and low frequency signal of $(0.29,0.15,0.37)$ in the dimensions respectively.\\ + Lew et al. (\cite{Lew14}) state low frequencies are mainly involved in spontaneous self-induced movement and can be found before the movement starts. By this they may be a great possibility to lower reaction time of neuroprostheses for example. %TODO: more details (idea, possible explanantion) + \subsection{Filtering} + Filtering of the recorded EEG signal is necessary for different reasons. First there are current relics from 50Hz current. These can be filtered out with bandstop filters.\\ + Secondly we need to concentrate on the interesting frequencies (for classical EEG 1-50Hz). This is done by applying lowpass or highpass filters respectively. This is necessary because the PSD of lower frequency is a lot higher than that of higher frequencies. The relation $$PSD(f)=\frac{c}{f^\gamma}$$ holds for constants $c$ and $\gamma$ (\cite{Demanuele07}).\\ + The Butterworth filter (\cite{Butterworth30}) was invented by Stephen Butterworth in 1930. It's advantage was uniform sensitivity to all wanted frequencies. In comparison to other filters Butterworth's is smoother because it is flat in the pass band and monotonic over all frequencies. This however leads to decreased steepness meaning a higher portion of frequencies beyond cutoff. \subsection{EMG} - Electromyography (EMG) is used to track muscle activity. This is done by measuring the electrical fields on the skin generated by muscle contraction. %TODO + When using muscles they are contracted after an signal via an efferent nerve activates them. Contraction of muscles also releases measurable energy which is used for Electromyography (EMG). There are intramuscular applications of EMG but we only used surface EMG.\\ + From surface EMG activity of muscles can be estimated however not very precisely without repetition. Since the muscles used for arm movements are quite large in our setting EMG allows relatively precise estimations of underlying muscle activity. + %TODO Use in science? \subsection{Synergies} Movement of the arm (and other parts of the body) are under-determined meaning with given trajectory there are different muscle contractions possible. One idea how this problem could be solved by our nervous system are synergies. Proposed by Bernstein in 1967 (\cite{Bernstein67}) they describe the goal of the movement (e.g. the trajectory) instead of controlling single muscles. This would mean however that predicting the activity of single muscles from EEG is harder than predicting a synergy which in turn determines the contraction of muscles.\\ Evidence for the use of synergies in the nervous system was found e.g. by Bizzi et al. (\cite{Bizzi08}) and Byadarhaly et al. (\cite{Byadarhaly12}). They also showed that synergies meet the necessary requirement to be able to build predictable trajectories.\\ @@ -51,7 +58,25 @@ \label{mat:autoenc} Autoencoders are a specific type of artificial neural networks (ANN). They work like typical ANNs by adjusting weights between the layers however they don't predict an unknown output but they predict their own input. What is interesting now is manipulating the size of the hidden layer where the size of the hidden layer has to be smaller than the input size. Now in the hidden layer the information of the input can be found in a condensed form (e.g. synergies instead of single muscle activity).\\ Autoencoders have been successfully used by Spüler et al. to extract synergies from EMG (\cite{Spueler16}). Especially with a lower number of synergies autoencoders perform better than PCA or NMF because linear models fail to discover the agonist-antagonist relations that are typical for muscle movements. These however can be detected by autoencoders which allows for good estimations with half the synergies. -\subsection{PCA} -\label{mat:pca} -\subsection{NMF} -\label{mat:nmf} + \subsection{PCA} + \label{mat:pca} + \subsection{NMF} + \label{mat:nmf} +\section{Experimental design} + The data used for this work were mainly recorded by Farid Shiman, Nerea Irastorza-Landa, and Andrea Sarasola-Sanz for their work (\cite{Shiman15},\cite{Sarasola15}). We were allowed to use them for further analysis.\\ + There were 9 right-handed subjects%TODO + All the tasks were performed with the right hand.\\ + To perform was a centre-out reaching task to one of four targets (see \ref{fig:experimentalDesign}) while 32 channel EEG, at least% + \footnote{\texttt{'AbdPolLo', 'Biceps', 'Triceps', 'FrontDelt', 'MidDelt'} and \texttt{'BackDelt'} were recorded for every subject, others only in some. Only the 6 channels tracked in every session were used}% + 6 channel surface EMG and 7 DOF kinematics were tracked. + \begin{figure} + \centering + \includegraphics{experimentalDesign.jpg} + \caption{Centre-out reaching task with four colour-coded targets} + \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.\\ + Only complete sessions were used in our analysis to ensure better comparability.\\ + One session consists of 5 runs with 40 trials each. The trials were separated by resting phases of varying length (2-3s, randomly assigned). Each trial began with an auditory cue specifying the random but equally distributed target for this trial. This leads to 50 reaches to the same target each session. + After the cue the participants should \qq{perform the movement and return to the starting position at a comfortable pace but within 4 seconds}\footnote{\cite{Shiman15}}\\ + For each subject there were 4 to 6 sessions, each recorded on a different day. All in all there were 255 runs in 51 sessions. Each session was analysed independently as one continuous task. diff --git a/text/thesis/mylit.bib b/text/thesis/mylit.bib index 3807162..c5580a9 100755 --- a/text/thesis/mylit.bib +++ b/text/thesis/mylit.bib @@ -122,7 +122,21 @@ volume = "7", year="2014" } - +@article{Vanhatalo04, + author = "Sampsa Vanhatalo and Juha Voipio and Kai Kaila", + year = "2004", + title = "Full-band EEG (FbEEG): an emerging standard in electroencephalography", + journal = "Clinical Neurophysiology", + volume = "116", + pages = "1-8" +} +@article{Lew14, + author = "Eileen Y. L. Lew and Ricardo Chavarriaga and Stefano Silvoni and José del R. Millán", + title = "Single trial prediction of self-paced reaching directions from EEG signals", + year = "2014", + journal = "Frontiers in Neuroscience", + volume = "8" +} @inproceedings{Sarasola15, author = {A. Sarasola-Sanz and N. Irastorza-Landa and F. Shiman and E. Lopez-Larraz and M. Spüler and N. Birbaumer and A. Ramos-Murguialday}, title = {EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies}, @@ -131,6 +145,28 @@ month = {08}, pages = {229-234}, } +@inproceedings{Shiman15, + author = {F. Shiman and N. Irastorza-Landa and A. Sarasola-Sanz and M. Spüler and N.Birbaumer and A. Ramos-Murguialday}, + title = {Towards Decoding of Functional Movements from the Same Limb using EEG}, + booktitle = {Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'15)}, + year = {2015}, + month = {08}, + pages = {1922-1925}, +} +@article{Demanuele07, + author = "Charmaine Demanuele and Christopher J James and Edmund JS Sonuga-Barke", + title = "Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals", + year = "2007", + journal = "Behav Brain Func" +} +@article{Butterworth30, + author = "S. Butterworth", + title = "On the Theory of Filter Amplifiers", + year = "1930", + journal = "Experimental wireless \& the wireless engineer", + volume = "7", + pages = "536-541" + } @article{Ting07,