diff --git a/text/thesis/01Introduction.tex b/text/thesis/01Introduction.tex index 443563a..d897f3e 100644 --- a/text/thesis/01Introduction.tex +++ b/text/thesis/01Introduction.tex @@ -8,9 +8,20 @@ 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 his arm and hand to modulate something.\\ Similar to that application it could be possible to drive a car by thought. This would lower the reaction time needed to activate the breaks for example. - Using non-invasive methods like EEG makes it harder to get a good signal and determine its origin. However it lowers the danger of injuries and infections which makes it the method of choice for wide spread application (cf. \cite{Collinger13}). Modern versions of these caps even use dry electrodes which allow for more comfort without loosing predictive strength (cf. \cite{Yeung15}). So everybody may put on and off an EEG-cap without high costs (e.g. for surgery). + Using non-invasive methods like EEG makes it harder to get a good signal 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 these caps even use dry electrodes which allow for more comfort without loosing predictive strength (cf. \cite{Yeung15}). So everybody may put on and off an EEG-cap without high costs (e.g. for surgery). Predicting synergies instead of positions or movement is nearer to the concept the nervous system uses. This should make them easier to predict while we can also use them to move an robotic arm or an quadrocopter. \section{Scientific background} \subsection{BCIs} - The idea of BCIs began to spread in the 1970s when Vidal published his paper (\cite{Vidal73}). + The idea of BCIs began to spread in the 1970s when Vidal published his paper (\cite{Vidal73}).\\ + First approaches used invasive BCIs earlier in Animals (rodents and monkeys) later also in humans. Invasive BCIs in humans were mostly implanted when the human was under brain surgery for another reason like epilepsy. Problems of invasive BCIs are the need to cut through skull and dura mater. This can lead to infections and severe brain damage.\\ + An improvement were less invasive BCIs with e.g. ECoG which is placed below the skull but outside the dura which decreased the risk for infections massively.\\ + Measuring outside the skull entails even less risk, the dura and skull however lower the quality of the signal massively. With some improvements EEG has a spatial resolution of 2-3 cm (cf. \cite{Babiloni01}). This is quite bad compared to the single neuron one can observe with invasive methods. However we are more interested in the activity of areas then single cells for our task, so EEG meets our requirements here. + \subsection{EEG} + When using EEG one measures the electrical fields on the scalp that are generated by activity of neurons in the brain. These measurements allow some interpretation about what is happening inside the skull. In our application we use the recorded currents directly to train a SVM or as predictor for regression.\\ + The frequencies typically used for movement prediction in EEG are about 8-24 Hz (\cite{Blokland15},\cite{Ahmadian13},\cite{Wang09}). + \subsection{Low Frequencies} + Another approach is looking at the low frequency features (below 1Hz) in the signal. %TODO citing + \subsection{Support Vector Machines (SVM)} + \subsection{Synergies} + diff --git a/text/thesis/mylit.bib b/text/thesis/mylit.bib index 1056291..b4a45d9 100755 --- a/text/thesis/mylit.bib +++ b/text/thesis/mylit.bib @@ -39,3 +39,29 @@ note = "Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE", pages = {7131-4}, } +@article{Babiloni01, + author = "Fabio Babiloni and Febo Cincotti and Filippo Carducci and Paolo M. Rossini and Claudio Babiloni", + title = "Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models", + year = "2001", + journal = "Clinical Neurophysiology", + volume = "112", + pages = "724-727", +} +@article{Blokland15, + author = "Yvonne Blokland and Loukianos Spyrou and Jos Lerou and Jo Mourisse and Gert Jan Scheffer and Geert-Jan van Geffen and Jason Farquhar and Jörgen Bruhn", + title = "Detection of attempted movement from the EEG during neuromuscular block: proof of principle study in awake volunteers", + year = "2015", + journal = "Scientific Reports", +} +@article{Wang09, + author = "Yijun Wang and Scott Makeig", + title = "Predicting Intended Movement Direction Using EEG from Human Posterior Parietal Cortex", + journal = "HCI International", + year = "2009", +} +@article{Ahmadian13, + author = "Pouya Ahmadian and Stefano Cagnoni and Luca Ascari1", + title = "How capable is non-invasive EEG data of predicting the next movement? A mini review", + year = "2013", + journal = "Frontiers in Human Neuroscience", +} diff --git a/text/thesis/outline.txt b/text/thesis/outline.txt index 8b01888..8458eb9 100644 --- a/text/thesis/outline.txt +++ b/text/thesis/outline.txt @@ -2,7 +2,7 @@ ## Introduction - Motivation - Techniques - - EMG + - BCI - EEG - LF - SVM @@ -27,6 +27,7 @@ - Neuronal Networks - PCA - NNMF +- Experiment data came from - My history - load data - Where to cut? diff --git a/text/thesis/thesis.tex b/text/thesis/thesis.tex index 068182f..1773e54 100644 --- a/text/thesis/thesis.tex +++ b/text/thesis/thesis.tex @@ -160,6 +160,8 @@ \textbf{EEG}\hspace{1cm}\=Electroencephalography\\ %\textbf{BMI}\> Brain-machine-interface \\ \textbf{BCI}\> Brain-computer-interface \\ +\textbf{SVM}\> Support-Vector-Machine \\ +\textbf{ECoG}\> Electrocorticography \\ \end{tabbing} \cleardoublepage