diff --git a/text/TODO.txt b/text/TODO.txt index 188460c..57aeccb 100644 --- a/text/TODO.txt +++ b/text/TODO.txt @@ -143,10 +143,10 @@ * vergleich EMG Synergie -* Acquisition -> preprocessing -* Yeung eher raus -* mean r : beide angeben -* Quelle zu 10-20 besser +* Acquisition -> preprocessing (done) +* Yeung eher raus (done) +* mean r : beide angeben (done) +* Quelle zu 10-20 besser (done) Vortrag diff --git a/text/thesis/01Introduction.tex b/text/thesis/01Introduction.tex index 8e51a79..d16bc66 100644 --- a/text/thesis/01Introduction.tex +++ b/text/thesis/01Introduction.tex @@ -9,7 +9,7 @@ In a slightly different context it might become possible to handle a machine (e.g. an industrial robot or mobile robots like quadrocopters) with \qq{thoughts} (i.e. brain activity) like an additional limb. One could learn to use the possibilities of the robot like the possibilities of one's arm and hand to modulate something.\\ Similar to that application it could be possible to drive a car by brain activity. This would lower the reaction time needed to activate the breaks for example by direct interaction instead of using the nerves down to the leg to press the break. - Using non-invasive methods like EEG makes it harder to get a good signal of brain activity and determine its origin. However, it lowers the risk of injuries and infections which makes it the method of choice for wide spread application (cf. \cite{Collinger13}). Modern versions of EEG-caps even use dry electrodes which allow for more comfort with similar predictive strength in context of movement of the whole body due to mathematical post-processing (cf. \cite{Yeung15}). So everybody may put on and off an EEG-cap without high costs for production or placement.\\ + Using non-invasive methods like EEG makes it harder to get a good signal of brain activity and determine its origin. However, it lowers the risk of injuries and infections which makes it the method of choice for wide spread application (cf. \cite{Collinger13}). There is also research in direction of EEG-caps with dry electrodes which allow for more comfort. In this field, however, much remains to be done (cf. \cite{Yeung15}). If working, everybody might put on and off an EEG-cap without high costs for production or placement.\\ With EEG brainwaves can be captured that let us predict intended movements. This movement predictions however bears some problems up to now. Predicting synergies instead of predicting positions or movement directly may solve some of these problems, since it is closer to the concept the nervous system uses. Most likely in brain there are no neurons for every single muscle involved in movement. Instead there are synergies activated, meaning there is coordinated co-activation of different muscles. When using synergies only some basic movements have to be represented in brain and can be combined for more complex movements.\\ @@ -18,7 +18,7 @@ This improvements shall be shown in this thesis. To do so, different methods of the acquisition of synergies from EMG are compared with other data and paradigms like direct prediction from EEG, EMG and low frequencies. \section{Overview}%TODO After this Introduction the scientific background and context of this work will be stated (Chapter \ref{chp:background}). This reaches from Principal Component Analysis (PCA) and Autoencoders over Support Vector Machines (SVMs) and regression to boxplots and topographical plots.\\ - Material and Methods (Chapter \ref{chp:mat}) shows the work done for tis thesis, beginning with the experimental design followed by the methods for data acquisition and analysis.\\ + Material and Methods (Chapter \ref{chp:mat}) shows the work done for tis thesis, beginning with the experimental design followed by the methods for data preprocessing and analysis.\\ In chapter \ref{chp:results} Results we show the numerical findings of our work separated into parts on synergies, classification, regression and a topographical analysis of the brain activity.\\ These results and their meaning will be discussed in chapter \ref{chp:dis} Discussion, which is concluded with a look into the possible future. diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex index 2069f68..8e1c1d3 100644 --- a/text/thesis/02MaterialsAndMethods.tex +++ b/text/thesis/02MaterialsAndMethods.tex @@ -26,8 +26,8 @@ The naming convention according to the 10-20 system is shown in figure~\ref{fig:10-20}. \begin{figure}[!p] \centering - \includegraphics[width=\textwidth]{eeg_electrodes_10-20.png} - \caption{Naming according to 10-20 system (Source: Marius 't Hart)} + \includegraphics[width=\textwidth]{pictures/eeg_electrodes_10-20.png} + \caption{Naming according to 10-20 system\\(Author: Marius 't Hart,\\License: https://creativecommons.org/licenses/by-sa/3.0/nl/deed.en\_GB)} \label{fig:10-20} \end{figure} \subsubsection{Frequency bands} @@ -139,7 +139,7 @@ \begin{figure} \centering \includegraphics[width=0.6\textwidth]{pictures/hyperplanes.png} - \caption{Two sets (red and blue) separated possible hyperplanes while the orange one has larger margins} + \caption{Two sets (red and blue) separated by possible hyperplanes where the orange one has larger margins} \label{fig:hyperplanes} \end{figure} The hyperplane is defined as $\vec{w}\cdot \vec{x}-b=0$ while the hyperplanes tangential to the classes are $\vec{w}\cdot \vec{x}-b=1$ or $\vec{w}\cdot \vec{x}-b=-1$ respectively (see Figure~\ref{fig:svm}). The margin is $\frac{2}{||\vec{w}||}$. The margins have to be maximized while all data is classified correctly, so the problem can be formulated as: @@ -262,7 +262,7 @@ One session consists of 5 runs with 40 trials each. The trials are separated by resting phases of varying length (2-3s, randomly assigned). Each trial is a grasp to one of four targets and begins 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 auditory cue the participants should \qq{perform the movement and return to the starting position at a comfortable pace but within 4 seconds} (\cite{Shiman15}).\\ For each subject there are 4 to 6 sessions, each recorded on a different day. All in all there are 255 runs in 51 sessions. Each session is analyzed independently as one continuous trial. -\section{Data Acquisition} +\section{Data Preprocessing} All the processing was done on Ubuntu \texttt{14.04 / 3.19.0-39} with \matlab{} \texttt{R2016a (9.0.0.341360) 64-bit (glnxa64) February 11, 2016}. \subsection{Loading of data} The data recorded with BCI2000 (\cite{Schalk04}) can be loaded into \matlab{} with a specific \texttt{.mex} file. The according \texttt{.mex}-Files for some platforms (Windows, MAC, Linux) are available from BCI2000 precompiled.\\ diff --git a/text/thesis/Bfunctions.tex b/text/thesis/Bfunctions.tex index 0ec910d..ab95e09 100644 --- a/text/thesis/Bfunctions.tex +++ b/text/thesis/Bfunctions.tex @@ -7,7 +7,7 @@ The default values for the parameters are given in table \ref{tab:default}. There are two independent scripts since this makes it possible to do calculations at the same time on the same machine and change called scripts without influencing the other run. In addition when calculating positions instead of velocities, some calculations do not need to be redone. Those are left out in \texttt{callAllPos.m}. -\section{Data Acquisition} +\section{Data Preprocessing} \subsection{\texttt{balanceClasses.m}} Balances classes e.g. for a SVM by dropping data from bigger classes. @@ -34,9 +34,9 @@ \texttt{namesAndNumbers} returns names and numbers of subjects and runs according to the file given (created by \ref{code:run.bash}) \subsection{\texttt{readAll.m}} \label{code:readAll} - This is the central function for the acquisition of data. + This is the central function for the preprocessing of data. - First, the name of the generated file is composed out of the given parameters. In this way the acquisition step only has to be done once.\\ + First, the name of the generated file is composed out of the given parameters. In this way the preprocessing only has to be done once.\\ If the file does not exist yet, it is created in the following steps:\\ Data from BCI2000 is read along the corresponding kinematic information. Then this data is transformed in the form we want to use it (cf. \texttt{generateTrainingData} \ref{code:generate}). The data from each of the five runs (cf. section~\ref{mm:design}) is aggregated in one variable per modality. diff --git a/text/thesis/eeg_electrodes_10-20.png b/text/thesis/eeg_electrodes_10-20.png deleted file mode 100644 index 7c4184e..0000000 --- a/text/thesis/eeg_electrodes_10-20.png +++ /dev/null Binary files differ diff --git a/text/thesis/eeg_electrodes_10-20.svg b/text/thesis/eeg_electrodes_10-20.svg deleted file mode 100644 index d2e1adf..0000000 --- a/text/thesis/eeg_electrodes_10-20.svg +++ /dev/null @@ -1,1778 +0,0 @@ - - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Nz - CPz - Fpz - AFz - Fz - FCz - Cz - Pz - POz - Oz - Iz - Fp1 - Fp2 - AF3 - AF4 - AF7 - AF8 - F7 - F5 - F3 - F1 - F2 - F4 - F6 - F8 - F9 - FT9 - FT7 - FC5 - FC3 - FC1 - FC2 - FC4 - FC6 - FC8 - F10 - FT10 - A1 - T9 - T7 - C5 - C3 - C1 - C2 - C4 - C6 - T8 - T10 - A2 - TP10 - P10 - TP8 - P8 - PO8 - O2 - PO4 - P2 - P4 - P6 - CP2 - CP4 - CP6 - TP9 - TP7 - CP5 - CP3 - CP1 - P9 - P7 - P5 - P3 - P1 - PO7 - PO3 - O1 - - Creative Commons: http://creativecommons.org/licenses/by-sa/3.0/nl/deed.en_GBAuthor: Marius 't Hart - http://www.beteredingen.nl -