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1

Using LORETA Method Based on the EEG Signal for Localizing the Sources of Brain Waves Activity

Paszkiel S.

Measurement Automation Monitoring

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2016

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Vol. 62, No. 8

262--264

EN

The article presents the possibility of using the method of imaging brain activity, LORETA LOw Resolution Electromagnetic TomogrAphy), that can base on electroencephalographical and magnetoencephalographical readings. Thanks to using the above-mentioned method, it is possible to localize the sources of the activity of individual signals registered on the head surface. This is very significant regarding construction of the brain-computer interfaces in order to conduct proper identification and classification of signals obtained during electroencephalography.

2

An attempt to localize brain electrical activity sources using EEG with limited number of electrodes

Majkowski A., Oskwarek Ł., Kołodziej M., Rak R. J.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

686--696

EN

A very interesting research goal is to find underlying sources generating the EEG signal–referred to as the ‘‘EEG inverse problem’’. Its aim is to determine spatial distribution of brain activity, described by local brain currents density, on the basis of potentials measured on the scalp as EEG signal. The purpose of the research presented in the article was to check whether the results of the inverse problem solution, obtained by the LORETA algorithm for the reduced set of 8 electrodes selected by the authors will be close to the results for the initial set of 32 electrodes. EEG signals were registered during the BCI operation based on ERD/ERS potentials. Obtained results showed no significant differences in the location of the most important sources in both cases. It is worth emphasizing that reducing the number of electrodes would have a significant impact on an BCI ergonomics.

3

Independent component analysis of EEG data for EGI system

Gajos A., Wójcik G. M.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 2

67--72

EN

Component analysis is one of the most important methods used for electroencephalographic (EEG) signal decomposition, and the so-called independent component analysis (ICA) is commonly used. The main function of the ICA algorithm is to find a linear representation of non-Gaussian data whose elements are statistically independent or at least as independent as possible. There are many commercial solutions for EEG signal acquisition. Usually, together with the EEG, one gets a dedicated software to handle the signal. However, quite often, the software does not provide researchers with all necessary functions. A high-performance, dense-array EGI-EEG system is distributed with the NetStation software. Although NetStation is a powerful tool, it does not have any implementation of the ICA algorithm. This causes many problems for researchers who want to export raw data from the amplifier and then work on it using some other tools such as EEGLAB for MATLAB, as these data are not fully compatible with the EGI format. We will present the C++ implementation of ICA that can handle filtered data from the EGI with better affordability. Our tool offers visualization of raw signal and ICA algorithm results and will be distributed under Freeware license.

4

Towards the Applications of Algorithms for Inverse Solutions in EEG Brain-Computer Interfaces

Jagodzińska U.

International Journal of Electronics and Telecommunications

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2013

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Vol. 59, No. 3

277-283

EN

Locating the sources of EEG signals (signal generators), i.e. indicating the places in the brain that the signals come from is the objective of the inverse problem in BCI applications using EEG. The two algorithms based on the methods used in the inverse problem: the linear least squares method and the LORETA1 method were compared. An analysis of the accuracy of locating the sources generating EEG signals on the basis of the two above mentioned methods was carried out with the use of the MATLAB programme. The findings made it possible to determine both the complexity of calculation involved in the methods under consideration and to compare the accuracy of the results obtained. Tests were done in which the inverse problem was solved on the basis of the data that were entered from the electrodes. Then potentials on electrodes were found by means of solving the forward problem once again ɸ (ɸ→ɸ). Moreover, tests were conducted on simulated data describing current density at selected places in the brain. In this case potentials on the electrodes were found by means of solving the forward problem. Subsequently the inverse problem was solved and potentials at selected places in the brain were specified J(J→Ĵ). In the case of J(J→Ĵ) only the relative error was examined, while the variance was studied in both cases. As a result of doing the tests, it was proved that relative errors were the same in the SVD and PINV methods, while in the LORETA method the error was similar. The variance computed for these methods was more differentiated for each of the cases, which made it possible to compare the algorithms in a better way. Differentiation of the variances under 0.2 shows that the algorithms that have been analyzed work properly. On the basis of knowing the results of the inverse problem, an attempt was made to make a selection of the best features of the EEG signal which differentiates the classes. In the present work tests were conducted to examine the differentiation of selected classes. Welch’s t-statistics was used to differentiate and order them. The results of the tests present the order for three classes of thought tasks, i.e. imagining moving one’s left hand, imagining moving one’s right hand, imagining generating words beginning with a randomly chosen letter. The present work is an introduction to a wider classification of features which are made with the use of inverse solutions.

5

Metoda LORETA jako przykład metody rozwiązywania zagadnienia odwrotnego w interfejsie mózg-komputer

Jagodzińska U., Oskwarek Ł.

Elektronika : konstrukcje, technologie, zastosowania

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2012

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Vol. 53, nr 12

89-91

PL

Metody rejestrowania elektrycznych i magnetycznych aktywności mózgu jak elektroencefalografia (EEG) lub magnetoencefalografia (MEG) nie dają pełnej informacji o źródle rejestrowanych sygnałów. Przyjmując uproszczony model źródeł jako dipoli prądowych umieszczonych w różnych punktach mózgu, trzeba mieć na uwadze, że istnieje nieskończenie wiele różnych konfiguracji tych źródeł, które generują taki sam rozkład potencjałów na powierzchni czaszki. Przeprowadzane testy, w których porównywano wyniki poszukiwań źródeł aktywności, dały najlepsze wyniki dla metody LORETA, np. przedstawione w pracy Pascual-Marqui R. D.

EN

Methods of register electrical and magnetic brain activities as electroencephalography (EEG) or magnetoencefalography (MEG) do not give information about exact sources of registered signals. If is assumed a simplified model of sources as current dipole placed at different points of the brain, it should be taken into account that there are infinitely many different configurations of these sources which generate the same distribution of potentials on the surface of the skull. Tests carried out for solving the EEG inverse problem which compared the results of exploration activity sources gave the best results for the LORETA method, examples in paper Pascual- Marqui R. D.