A Software Package for the Modeling of Electrophysiological Activity Data

A complex of programs has been developed for computer modeling of multichannel time series recorded in various experiments on electromagnetic fields created by the human body. Sets of coordinates and directions of sensors for magnetic encephalographs of several types, electroencephalographs and magnetic cardiographs are used as models of devices. To study the human brain, magnetic resonance tomograms are used as head models; to study the heart, a body model in the form of a half-space with a flat boundary is used. The sources are placed in the model space, for them the direct problem is solved in the physical model corresponding to the device used. For a magnetic encephalograph and an electroencephalograph, an equivalent current dipole model in a spherical conductor is used, for a magnetic cardiograph, an equivalent current dipole model in a flat conductor or a magnetic dipole model is used. For each source, a time dependence is set and a multichannel time series is calculated. Then the time series from all sources are summed and the noise component is added. The program consists of three modules: an input-output module, a calculation module and a visualization module. The input-output module is responsible for loading device models, brain models, and field source parameters. The calculation module is responsible for directly calculating the field and transforming coordinates between the index system and the head system. The visualization module is responsible for the image of the brain model, the position of the field sources, a graphical representation of the amplitude-time dependence of the field sources and the calculated values of the total field. The user interface has been developed. The software package provides: interactive placement of field sources in the head or body space and editing of the amplitude-time dependence; batch loading of a large number of sources; noise modeling; simulation of low-channel planar magnetometers of various orders, specifying the shape of the device, the number of sensors and their parameters. Magnetic and electric fields produced by sources in the brain areas responsible for processing speech stimuli are considered. The resulting multichannel signal can be used to test various data analysis methods and for the experiment planning.

Modelling Secular Variation in the Southwest Pacific for the last 400 years

<p>A spherical cap harmonic analysis (SCHA) model has been used to derive a high-resolution regional model of the geomagnetic field in the southwest Pacific region over the past 400 years. Two different methods, a self-consistent and the gufm1 dipole method, have been used to fill in gaps in the available data. The data used in the analysis were largely measurements of the magnetic field recorded in ships logs on voyages of exploration in the region. The method chosen for the investigation used a spherical cap of radius 𝜃₀ = 50° centered at co-latitude and longitude of (115°, 160°). The results of each method used for SCHA are presented as contour plots of magnetic field declination, inclination and intensity and are compared with similar plots for a global model, gufm1. The root mean square misfit of the self- consistent and gufm1 dipole model to the actual data were around 2900 nT and 23000 nT respectively. Overall, the results suggest that the self-consistent model produces a more reliable model of the geomagnetic field within the area of interest than does the gufm1 dipole model. With more data included the self-consistent model could be further improved and used to develop a high-resolution mathematical model of the geomagnetic field in the southwest Pacific region.</p>

Modelling Secular Variation in the Southwest Pacific for the last 400 years

<p>A spherical cap harmonic analysis (SCHA) model has been used to derive a high-resolution regional model of the geomagnetic field in the southwest Pacific region over the past 400 years. Two different methods, a self-consistent and the gufm1 dipole method, have been used to fill in gaps in the available data. The data used in the analysis were largely measurements of the magnetic field recorded in ships logs on voyages of exploration in the region. The method chosen for the investigation used a spherical cap of radius 𝜃₀ = 50° centered at co-latitude and longitude of (115°, 160°). The results of each method used for SCHA are presented as contour plots of magnetic field declination, inclination and intensity and are compared with similar plots for a global model, gufm1. The root mean square misfit of the self- consistent and gufm1 dipole model to the actual data were around 2900 nT and 23000 nT respectively. Overall, the results suggest that the self-consistent model produces a more reliable model of the geomagnetic field within the area of interest than does the gufm1 dipole model. With more data included the self-consistent model could be further improved and used to develop a high-resolution mathematical model of the geomagnetic field in the southwest Pacific region.</p>

Finding exceptional points in realistic systems using full-wave simulations

Here, we present an approach to finding exceptional points using the finite-element method. Using this method, we demonstrated exceptional points in 2D dimers of infinite cylinders and infinite parallelepipeds. The results agree well with the analytical coupled-dipole model, however a deviation due to the contribution of higher multipoles, is present. Our approach can be applied to three-dimensional particles as well.

ConvDip: A Convolutional Neural Network for Better EEG Source Imaging

The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs &lt;40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.

A Simple and Novel Localization Method Using the Radiated Ultra-Low-Frequency Electromagnetic Wave Signals from the Surface Vehicle and Submerged Ones

Because the surface and submerged vehicles radiate Ultra-Low-Frequency (ULF) Electromagnetic waves, the status of the vehicles in the ocean can be detected and explored by analyzing such signals, and this has been gained increasing attention. In this paper, a hybrid algorithm of the ant colony algorithm and Levenberg–Marquardt algorithm is proposed to locate a moving target with a constant speed based on the fully investigation of the uniformly magnetized spheroid model. Additionally, an experiment has been conducted to validate the performance of the hybrid algorithm. At the same time, the comparison between the proposed ellipsoid model with the conventional dipole model has also been done, and the results show that the calculated results based on the prolate spheroid model agree well with the recorded GPS results with maximum 6.67% average error, which is way better than the dipole model (31.59%, max.).