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

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.

A novel approach of CNN for human motor imagery recognition using the virtual electrode pairs

In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability.

A Three-Dimensional Microelectrode Array to Generate Virtual Electrodes for Epiretinal Prosthesis Based on a Modeling Study

Despite many advances in the development of retinal prostheses, clinical reports show that current retinal prosthesis subjects can only perceive prosthetic vision with poor visual acuity. A possible approach for improving visual acuity is to produce virtual electrodes (VEs) through electric field modulation. Generating controllable and localized VEs is a crucial factor in effectively improving the perceptive resolution of the retinal prostheses. In this paper, we aimed to design a microelectrode array (MEA) that can produce converged and controllable VEs by current steering stimulation strategies. Through computational modeling, we designed a three-dimensional concentric ring–disc MEA and evaluated its performance with different stimulation strategies. Our simulation results showed that electrode–retina distance (ERD) and inter-electrode distance (IED) can dramatically affect the distribution of electric field. Also the converged VEs could be produced when the parameters of the three-dimensional MEA were appropriately set. VE sites can be controlled by manipulating the proportion of current on each adjacent electrode in a current steering group (CSG). In addition, spatial localization of electrical stimulation can be greatly improved under quasi-monopolar (QMP) stimulation. This study may provide support for future application of VEs in epiretinal prosthesis for potentially increasing the visual acuity of prosthetic vision.

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

A new method of analyzing magnetic encephalography data, the virtual electrode method, was developed. According to magnetic encephalography data, a functional tomogram is constructed — the spatial distribution of field sources on a discrete grid. A functional tomogram displays on the head space the information contained in the multichannel time series of an encephalogram. This is achieved by solving the inverse problem for all elementary oscillations extracted using the Fourier transform. Each oscillation frequency corresponds to a three-dimensional grid node in which the source is located. The user sets the location, size and shape of the brain area for a detailed study of the frequency structure of a functional tomogram - a virtual electrode. The set of oscillations that fall into a given region represents the partial spectrum of this region. The time series of the encephalogram measured by the virtual electrode is restored using this spectrum. The method was applied to the analysis of magnetic encephalography data in two variations - a virtual electrode of a large radius and a point virtual electrode.

Automatic control of grasping strength for functional electrical stimulation in forearm movements via electrode arrays

The generation of precise hand movements with functional electrical stimulation (FES) via surface electrodes on the forearm faces several challenges. Besides the biomechanical complexity and the required selectivity, the rotation of the forearm during reach-and-grasp tasks leads to a relative change between the skin and underlying tissue, resulting in a varying FES response. We present a new method for automatic adaptation of virtual electrodes (size, position) and stimulation intensity in an electrode array to guarantee a secure grasp during forearm movements. The method involves motion tracking of arm and hand with inertial sensors. This enables the estimation of grasping strength when using elastic objects. Experiments in healthy volunteers revealed that our method allows generating a strong, stable grasp force regardless of the rotational state of the forearm.