VirtEl - Software for Magnetic Encephalography Data Analysis by the Method of Virtual Electrodes

Рыкунов, С.Д.; Rykunova, E.D.; Boyko, A.I.; Ustinin, M.N.

doi:10.17537/2019.14.340

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Каждая ячейка функциональной томограммы содержит индивидуальный спектр, а также сведения о направлениях найденных источников [26]. Функциональная томограмма позволяет изучать электромагнитные сигналы мозга с любой степенью подробности, выделяя для анализа произвольно заданные области [34]. Это открывает новые возможности для изучения спонтанной активности мозга человека с помощью электроэнцефалографии.…”

Section: заключениеunclassified

Reconstruction of the Human Brain Functional Structure Based on the Electroencephalography Data

Ustinin¹,

Рыкунов²,

Boyko³

et al. 2020

Math.Biol.Bioinf.

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New method for the data analysis was proposed, making it possible to transform multichannel time series into the spatial structure of the system under study. The method was successfully used to investigate biological and physical objects based on the magnetic field measurements. In this paper we further develop this method to analyze the data of the experiments where the electric field is measured. The brain activity in the state of subject “eyes closed” was registered by the 19-channel electric encephalograph, using the 10-20 scheme. The electroencephalograms were obtained in resting state and with arbitrary hands motions. Detailed multichannel spectra were obtained by the Fourier transform of the whole time series. All spectral data revealed the broad alpha rhythm peak in the frequency band 9-12 Hz. For all spectral components in this band the inverse problem was solved, and the 3D map of the brain activity was calculated. The inverse problem was solved in elementary current dipole model for one-layer spherical conductor without any restrictions for the source position. The combined analysis of the magnetic resonance image and the brain functional structure leads to the conclusion that this structure generally corresponds to the modern knowledge about the alpha rhythm. The 3D map of the vector field of the dominating directions of the alpha rhythm sources was also generated. The proposed method can be used to study the spatial distribution of the brain activity in any spectral band of the electroencephalography data.

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Section: заключениеunclassified

Reconstruction of the Human Brain Functional Structure Based on the Electroencephalography Data

Ustinin¹,

Рыкунов²,

Boyko³

et al. 2020

Math.Biol.Bioinf.

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“…В работе [31] был предложен метод виртуальных электродов, основанный на том, что функциональная томография ставит в соответствие каждой элементарной осцилляции пространственное положение ее источника. Выбирая область пространства и все источники, находящиеся в ней, по их частотам и коэффициентам разложения Фурье можно восстановить многоканальный временной ряд, получая таким образом энцефалограмму выбранной области.…”

Section: метод функциональной томографии и метод виртуальных электродовunclassified

“…№ 2. doi: 10.17537/2020.15.471 паттернов магнитного поля выбираются те паттерны, индексные координаты которых соответствуют индексным координатам маски рассматриваемого отдела. Частоты и коэффициенты Фурье этих паттернов далее интерпретируются как показания виртуального электрода [31], соответствующего рассматриваемому компартменту мозга. На рисунке 1 показаны разделы мозга, изучавшиеся в данной работе.…”

Section: результатыunclassified

Correlation of the Brain Compartments in the Attention Deficit and Hyperactivity Disorder Calculated by the Method of Virtual Electrodes from Magnetic Encephalography Data

Ustinin¹,

Boyko²,

Рыкунов³

2020

Math.Biol.Bioinf.

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New method to study the correlation of the human brain compartments based on the magnetic encephalography data analysis was proposed. The time series for the correlation analysis are generated by the method of virtual electrodes. First, the multichannel time series of the subject with confirmed attention deficit and hyperactivity disorder are transformed into the functional tomogram - spatial distribution of the magnetic field sources structure on the discrete grid. This structure is provided by the inverse problem solution for all elementary oscillations, found by the Fourier transform. Each frequency produces the elementary current dipole located in the node of the 3D grid. The virtual electrode includes the part of space, producing the activity under study. The time series for this activity is obtained by the summation of the spectral power of all sources, covered by the virtual electrode. To test the method, in this article we selected ten basic compartments of the brain, including frontal lobe, parietal lobe, occipital lobe and others. Each compartment was included in the virtual electrode, obtained from the subjects' MRI. We studied the correlation between compartments in the frequency bands, corresponding to four brain rhythms: theta, alpha, beta, and gamma. The time series for each electrode were calculated for the period of 300 seconds. The correlation coefficient between power series was calculated on the 1 second epoch and then averaged. The results were represented as matrices. The method can be used to study correlations of the arbitrary parts of the brain in any spectral band.

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Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography

Rykunov,

Boyko,

Ustinin

2023

Pattern Recognit. Image Anal.

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VirtEl - Software for Magnetic Encephalography Data Analysis by the Method of Virtual Electrodes

Cited by 3 publications

References 24 publications

Reconstruction of the Human Brain Functional Structure Based on the Electroencephalography Data

Reconstruction of the Human Brain Functional Structure Based on the Electroencephalography Data

Correlation of the Brain Compartments in the Attention Deficit and Hyperactivity Disorder Calculated by the Method of Virtual Electrodes from Magnetic Encephalography Data

Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography

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