scholarly journals A Software Package for the Modeling of Electrophysiological Activity Data

Author(s):  
A.I. Boyko ◽  
S.D. Rykunov ◽  
M.N. Ustinin

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.

Author(s):  
M.N. Ustinin ◽  
S.D. Rykunov ◽  
A.I. Boyko ◽  
O.A. Maslova

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.


Author(s):  
M.N. Ustinin ◽  
S.D. Rykunov ◽  
A.I. Boyko ◽  
O.A. Maslova ◽  
N.M. Pankratova

New method for the magnetic encephalography data analysis was proposed, making it possible to transform multichannel time series into the spatial structure of the human brain activity. In this paper we applied this method to the analysis of magnetic encephalograms, obtained from subjects with attention deficit and hyperactivity disorder. We have considered the experimental data, obtained with 275-channel magnetic encephalographs in McGill University and Montreal University. Magnetic encephalograms of the brain spontaneous activity were registered for 5 minutes in magnetically shielded room. Detailed multichannel spectra were obtained by the Fourier transform of the whole time series. For all spectral components, the inverse problem was solved in elementary current dipole model and the functional structure of the brain activity was calculated in the broad frequency band 0.3-50 Hz. It was found that frequency band relations are different in different experiments. We proposed to use these relations by the summary electric power produced by the sources in selected frequency band. The delta rhythm in frequency band 0.3 to 4 Hz was studied in detail. It was found, that many delta rhythm dipoles were localized outside the brain, and their spectrum consists of the heartbeat harmonics. It was concluded that in experiments considered, the delta rhythm represents the vascular activity of the head. To study the spatial distribution of all rhythms from theta to gamma the partial spectra of the brain divisions were calculated. The partial spectrum includes all frequencies produced by the dipole sources located in the region of brain selected at the magnetic resonance image. The method can be further applied to study encephalograms in various psychic disorders.


NeuroImage ◽  
2008 ◽  
Vol 39 (2) ◽  
pp. 728-741 ◽  
Author(s):  
Stefan J. Kiebel ◽  
Jean Daunizeau ◽  
Christophe Phillips ◽  
Karl J. Friston

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.


2021 ◽  
Author(s):  
Abhishek S. Bhutada ◽  
Chang Cai ◽  
Danielle Mizuiri ◽  
Anne Findlay ◽  
Jessie Chen ◽  
...  

AbstractMagnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.


2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang

Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3345
Author(s):  
Enrico Zero ◽  
Chiara Bersani ◽  
Roberto Sacile

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.


Sign in / Sign up

Export Citation Format

Share Document