Czasopismo | ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA | Rocznik 2013 - zeszyt 9

Applying algorithms for inverse solutions in classifying EEG signals

Urszula Jagodzińska

nr katalogowy: 79337

The EE G recording was obtained through the system of electrodes located on the scalp (a non-invasive method). All the calculations were made according to the Idiap (Idiap Research Institute Switzerland (Silvia Ciappa, José del R. Millán) signals, i.e. 32 electrodes were taken into account, while in the case of preprocessed data, the electrodes C2, Cz, C4, CP 1, CP 2, P3, P4 and Pz were examined. In this system the locations of electrodes are related to specific brain areas. The spherical head model was adopted. It was assumed that the location of the electrodes is correlated to the brain areas which were taken into account in the experiment described below. Such a brain location is only a broadly approximate one but correcting the place where the electrodes are attached can reduce this approximation thanks to the solution of the inverse problem. Better correlation can also be received when magnetic resonance imaging (MRI ) is used, because there the geometry of each specific person being examined is studied. It is obvious that the signal received by one electrode can come from different areas of the brain, and besides they can be influenced by muscle movements and e.g. movements of the eye-ball and that is why the preprocessed signal is being used. Studying the location of brain areas and their functions based on the solution to the inverse problem by means of using various algorithms is presented in the next part of this article. The BCI applications using EE G are based on extracting the features from the EEG signal. The next steps are the selection of features and their classification. To improve the classification results it is possible to add information acquired by solving the inverse problem to receive, e.g. better discrimination of the intensity of signals received from different parts of the brain. The classification is based on the reconstructed sources of brain activity which are being received after solving the i[...]

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