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ResearchDayPoster.tex
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\documentclass[serif,final]{beamer}
\mode<presentation>{\usetheme{Lankton}}
\usepackage{amsmath,amsfonts,amssymb,pxfonts,xspace}
\usepackage{graphicx}
\graphicspath{{./figures/}}
\usepackage[orientation=landscape,size=a0, scale=1.8]{beamerposter}
\setbeamertemplate{caption}[numbered]
%tikz
\usepackage{tikz}
\usetikzlibrary{shapes,calc,arrows,shadows}
\usepackage{amsmath,bm,times}
\newcommand{\mx}[1]{\mathbf{\bm{#1}}} % Matrix command
\newcommand{\vc}[1]{\mathbf{\bm{#1}}} % Vector command
%-- Header and footer information ----------------------------------
\newcommand{\footleft}{http://www.aalto.fi}
\newcommand{\footright}{[email protected]}
\title{\textit{BrainTrack} : Dynamic identification of brain networks \\ by Bayesian tracking of electrophysiological data}
\author{Narayan Subramaniyam$^{1}$ \quad Filip Tronarp$^{2}$ \quad Simo S\"arkk\"a$^{2}$ \quad Lauri Parkkonen$^{1}$}
\institute{$^{1}$ Department of Neuroscience and Biomedical Engineering \\ $^{2}$ Department of Electrical Engineering and Automation}
%-------------------------------------------------------------------
%-- Main Document --------------------------------------------------
\begin{document}
\begin{frame}{}
\begin{columns}[t]
%-- Column 1 ---------------------------------------------------
\begin{column}{0.45\linewidth}
%-- Block 1-1
\begin{block}{Introduction}
\begin{flushleft}
\textbf{\color{blue} BrainTrack} is an Academy of Finland -funded project (2015--2019) with the \\ aim to develop a novel method to estimate \textbf{\color{blue}functional brain networks} from magnetoencephalographic (MEG) as well as from scalp and intracranial electroencephalographic (EEG) recordings using \textbf{\color{blue} Bayesian tracking} \cite{sarkka2013bayesian}.
\end{flushleft}
\begin{figure}
\centering
\includegraphics[scale=0.75]{intro_1}
\caption{Joint estimation of sources and network structure from non-invasive MEG/EEG recordings.}
\label{fig1}
\end{figure}
The computational core of BrainTrack is a \textbf{\color{blue} spatio--temporal marginalized particle filter} algorithm \cite{sarkka2013bayesian} that will estimate the network structure along with source parameters. The Bayesian model for the measurements is based on our previous work \cite{sorrentino2009dynamical,chen2015bayesian}.
\end{block}
%-- Block 1-2
\begin{block}{Significance}
\begin{enumerate}
\setlength\itemsep{0.25em}
\item Tools for better characterization of epileptic activity as a \textbf{\color {blue} dynamic functional network} to aid the accurate \textbf{\color {blue} localization} of \textbf{\color {blue} epileptic foci}.
\item Real-time connectivity estimation for \textbf{\color {blue} neurofeedback} experiments.
\end{enumerate}
\end{block}
%-- Block 1-3
\begin{block}{International collaboration}
The project will be done in collaboration with
\begin{itemize}
\small
\item \textit{Asahikawa Medical University}, Japan (Combined intracranial EEG and MEG recordings)
\item \textit{University of Cambridge}, UK (Bayesian particle filtering methodology)
\item \textit{McGill University}, Canada (Interpretation of connectivity measures and neurofeedback experiments)
\item \textit{Universit\'e de Montr\'eal}, Canada (Interpretation of connectivity in pathological conditions, intracranial-EEG + MEG recordings )
\end{itemize}
\end{block}
\end{column}%1
%-- Column 2 ---------------------------------------------------
\begin{column}{0.5\linewidth}
%-- Block 2-1
\begin{block}{research Framework}
\begin{figure}
\vspace{2cm}
\centering
\includegraphics[width = 0.4\linewidth]{framework_ver2}
% \caption{General framework of the project depicting the workflow}
\end{figure}
\begin{tikzpicture}[remember picture,overlay]
\node[anchor=south west,inner sep=0pt] at ($(current page.south west)+(58cm,38cm)$) {
\includegraphics[scale=0.6]{data} };
\end{tikzpicture}
\begin{tikzpicture}[remember picture,overlay]
\node[anchor=south west,inner sep=0pt] at ($(current page.south west)+(68cm,47cm)$) {
\includegraphics[scale=0.43]{mri} };
\end{tikzpicture}
\begin{tikzpicture}[remember picture,overlay]
\node[anchor=south west,inner sep=0pt] at ($(current page.south west)+(99cm,37cm)$) {
\includegraphics[scale=0.6]{connectivity} };
\end{tikzpicture}
\begin{tikzpicture}[remember picture,overlay]
\node[anchor=south west,inner sep=0pt] at ($(current page.south west)+(78cm,56cm)$) {
\includegraphics[scale=0.4]{eegmeg} };
\end{tikzpicture}
\begin{tikzpicture}[remember picture,overlay]
\node[anchor=south west,inner sep=0pt] at ($(current page.south west)+(93.5cm,47cm)$) {
\includegraphics[scale=0.4]{bem} };
\end{tikzpicture}
\vspace{-13cm}
\end{block}
%-- Block 2-2
\begin{block}{Expected Results and Impact}
\begin{enumerate}
\setlength\itemsep{0.25em}
\item A platform for \textbf{\color {blue} accurate, real-time} estimation of functional brain connectivity from electrophysiological data.
\item Clinical applications: \textbf{\color {blue} Characterization} of spreading pathological brain activity in \textbf{ \color {blue} network disorders} such as epilepsy.
\item Neuroscience applications: Identification of functional brain networks supporting various \textbf{\color {blue} cognitive functions}.
\end{enumerate}
\end{block}
%-- Block 2-2
\begin{block}{References}
\footnotesize
\bibliographystyle{ieeetr}
\bibliography{refs}
\end{block}
\end{column}%2
\end{columns}
\end{frame}
\end{document}