diff --git a/text/ideas.txt b/text/ideas.txt new file mode 100644 index 0000000..6cfe922 --- /dev/null +++ b/text/ideas.txt @@ -0,0 +1,3 @@ +explanation for bad position/movement estimation: +* not known whether up or down -> gravitational influence +* better to estimate EMG since this corresponds to muscle activity diff --git a/text/thesis/02MaterialsAndMethods.tex b/text/thesis/02MaterialsAndMethods.tex index 4199903..c14073f 100644 --- a/text/thesis/02MaterialsAndMethods.tex +++ b/text/thesis/02MaterialsAndMethods.tex @@ -26,10 +26,17 @@ \caption{Full 10-20 system} \label{fig:10-20} \end{figure} - \subsubsection{\texttt{pburg}} - \label{mat:pburg} - For power spectral density estimation (PSD) of the EEG signal we used MATLAB's pburg method. This uses Burg's method, a parametrized method of PSD.\\ - %... see bookmarks + \subsection{Power estimation} + \subsubsection{EEG} + To use data from EEG one way is to analyse the occurring frequencies and their respective power.\\ + To gain these from the continuous signal there are different methods. The intuitive approach would be to use Fourier transformation however the Fourier transform does not need to exists for a continuous signal. So we used power spectral density (PSD) estimation. + \subsubsection{Power spectral density estimation} + The PSD is the power per frequency. Power here refers to the square of the amplitude. %TODO: formulation, additional explanation?, fft + If the Fourier transform is existing, PSD can be calculated from it e.g. as periodogram. If not it has to be estimated. One way to do so is parametrised with an Autoregressive model. Here one assumes that the there is a correlation between $p$ consecutive samples and the one following of the spectral density. This leads to an equation with only $p$ parameters which can be estimated in different ways. We used Burg's method (\texttt{pburg} from MATLAB library). + \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 \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. diff --git a/text/thesis/eeg_electrodes_10-20.svg b/text/thesis/eeg_electrodes_10-20.svg new file mode 100644 index 0000000..d2e1adf --- /dev/null +++ b/text/thesis/eeg_electrodes_10-20.svg @@ -0,0 +1,1778 @@ + + + + + + + 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 + diff --git a/text/thesis/mylit.bib b/text/thesis/mylit.bib index ca7cf01..3807162 100755 --- a/text/thesis/mylit.bib +++ b/text/thesis/mylit.bib @@ -108,10 +108,23 @@ volume = "32", pages = "96-108" } - +@phdthesis{Burg75, + author = "John Parker Burg", + title = "Maximum Entropy Spectral Analysis", + year = "1975", + month = "May", + school = "Stanford University," +} +@article{Huang14, + author="Junyou Huang", + title= "Study of Autoregressive (AR) Spectrum Estimation Algorithm for Vibration Signals of Industrial Steam Turbines", + journal ="International Journal of Control and Automation", + volume = "7", + year="2014" +} @inproceedings{Sarasola15, - author = {A. Sarasola-Sanz, N. Irastorza-Landa, F. Shiman, E. Lopez-Larraz, M. Spüler, N. Birbaumer, A. Ramos-Murguialday}, + 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}, booktitle = {IEEE International Conference on Rehabilitation Robotics (ICORR)}, year = {2015}, @@ -120,7 +133,6 @@ } - @article{Ting07, author = "Lena H. Ting and J Lucas McKay", title = "Neuromechanics of muscle synergies for posture and movement", diff --git a/text/thesis/thesis.out b/text/thesis/thesis.out new file mode 100644 index 0000000..fd7c4fe --- /dev/null +++ b/text/thesis/thesis.out @@ -0,0 +1,18 @@ +\BOOKMARK [0][-]{chapter*.3}{List of Figures}{}% 1 +\BOOKMARK [0][-]{chapter*.4}{List of Tables}{}% 2 +\BOOKMARK [0][-]{chapter*.5}{List of Abbreviations}{}% 3 +\BOOKMARK [0][-]{chapter.1}{Introduction}{}% 4 +\BOOKMARK [1][-]{section.1.1}{Motivation}{chapter.1}% 5 +\BOOKMARK [1][-]{section.1.2}{Overview}{chapter.1}% 6 +\BOOKMARK [0][-]{chapter.2}{Materials and Methods}{}% 7 +\BOOKMARK [1][-]{section.2.1}{Scientific background}{chapter.2}% 8 +\BOOKMARK [2][-]{subsection.2.1.1}{BCIs}{section.2.1}% 9 +\BOOKMARK [2][-]{subsection.2.1.2}{EEG}{section.2.1}% 10 +\BOOKMARK [2][-]{subsection.2.1.3}{Power estimation}{section.2.1}% 11 +\BOOKMARK [2][-]{subsection.2.1.4}{Low Frequencies}{section.2.1}% 12 +\BOOKMARK [2][-]{subsection.2.1.5}{EMG}{section.2.1}% 13 +\BOOKMARK [2][-]{subsection.2.1.6}{Synergies}{section.2.1}% 14 +\BOOKMARK [2][-]{subsection.2.1.7}{Autoencoders}{section.2.1}% 15 +\BOOKMARK [2][-]{subsection.2.1.8}{PCA}{section.2.1}% 16 +\BOOKMARK [2][-]{subsection.2.1.9}{NMF}{section.2.1}% 17 +\BOOKMARK [0][-]{subsection.2.1.9}{Bibliography}{}% 18 diff --git a/text/thesis/thesis.tex b/text/thesis/thesis.tex index 0f899a0..00cc5f8 100644 --- a/text/thesis/thesis.tex +++ b/text/thesis/thesis.tex @@ -165,6 +165,7 @@ \textbf{PCA}\> Principal Component Analysis \\ \textbf{NMF}\> non-Negative Matrix Factorisation \\ \textbf{ANN}\> Artificial Neural Network \\ +\textbf{PSD}\> Power Spectral Density \\ \end{tabbing} \cleardoublepage @@ -204,6 +205,8 @@ \addcontentsline{toc}{chapter}{Bibliography} +\nocite{*} + \bibliographystyle{alpha} \bibliography{mylit} %% Obige Anweisung legt fest, dass BibTeX-Datei `mylit.bib' verwendet