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Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

Wójcik Grzegorz M., Kawiak Andrzej, Kwasniewicz Lukasz, Schneider Piotr, Masiak Jolanta

Bio-Algorithms and Med-Systems

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2019

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Vol. 15, no. 3

art. no. 20190031

EN

The Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.

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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.

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Head models for a localization of the active sources within the brain

Orzechowski M., Dunajski Z.

Elektronika : konstrukcje, technologie, zastosowania

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2004

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Vol. 45, nr 8-9

94-96

EN

The brain with its surrounding tissues is a multicompartment electrical conductor. The activity is observed as distribution of electric potentials on a scalp and as magnetic field over the head. The different methods of head modelling for a localization of the active sources within the brain based on EEG or MEG recordings were discussed as well as the advantages and drawbacks of the described models.

PL

Mózg wraz z otaczającymi go tkankami stanowi przewodnik objętościowy zawierający struktury aktywne elektrycznie. Odzwierciedleniem aktywności ośrodków pobudzenia w mózgu jest rozkład potencjałów na powierzchni skóry głowy oraz pola magnetycznego w przestrzeni dookola głowy. W niniejszym artykule opisane zostały różne metody modelowania głowy dla potrzeb lokalizacji ośrodków pobudzenia w mózgu na podstawie zapisów EEG lub MEG oraz omówione zalety i niedostatki poszczególnych modeli.