Figure 2 - uploaded by Radu Ranta
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Partial head mesh for the studied epileptic patient. To ease the visualization, only the scalp (in gray) and the inner skull (in red) are represented, along with the dipolar layer (in blue) used for REST (here at 2mm, 4mm, 6mm and 8mm depth below the scalp surface).
Source publication
A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2...
Context in source publication
Context 1
... REST solutions were computed for the absolute potentials, for dipolar layers placed at different depths (2mm, 4mm, 6mm, 8mm and 10mm) with respect to the head surface, but outside the brain (inner skull) mesh (see Figure 2). The geometry of the layer was the same as the one of the scalp, in order to be able to keep a constant distance between the sensors and the layer, except in the lower part of the brain, where we considered a flat surface 10 mm outside the inner skull. ...
Citations
... The accuracy of this method is not much higher than the accuracy of the measurements when the earlobe is used as a reference point. Salido-Ruiz et al. [3] developed the direct EEG model based on microstates and the use of various references for microstate analysis. But this model requires high-precision auxiliary equipment to the measurement process. ...
An unstable reference point is a critical problem for electroencephalography (EEG) research since its instability introduces a significant distortion into the interpretation of the EEG signal. The main requirement for a reference point-electric potential should be unchanged. This is extremely difficult to implement in practice, due to biological processes occurring in the human body. Unlike other artifacts (network noise, electrical noise, blinking) that can be solved both by using analog circuitry (bandpass filters for noise, etc.) and by using various mathematical methods (method of head components for eye blink, etc.), a change in the potential of the reference point during the EEG measurement will be present in any case. Today, for a reference point an earlobe and overall average referential reference are used. The problems of using these methods are described in many works. In this manuscript, we propose a new method of using the reference point, electric potential which is generated by a 24-bit digital-to-analog converter (DAC). At the initial stage, a 24-bit analog-to-digital converter (ADC) reads the potential between the earlobes, and then on the STM 32 microcontroller through the DAC it generates such potential with an accuracy of 0.1 μV. From this moment, to calculate the voltage at the electrode, the voltage at the output of the DAC is used as the reference potential. Subsequently, the ADC makes comparisons of the potential between two points in the earlobes, which ideally should equal 0 V. In the case, if the difference between earlobes voltage is more than 0.1 μV, the DAC starts to compensate this value at its output, thereby averaging the value between the two earlobes. Algorithms in software to exclude instantaneous changes in potential on the earlobe were written in the STM32. Algorithms eliminate the uncontrolled effect of an increase of neuronal activity in the temporal region of the head. Thus, the developed prototype by DAC of the device replaces the potential on the earlobes and, based on mathematical calculations, provides a stable reference voltage for calculating the voltage on the electrodes.
... The same reasoning holds for Laplacian / CSD estimates of local sources (Mitzdorf, 1985;Hjorth, 1975). Moreover, because the positions are not known, a forward-inverse model based solution like REST (Yao, 2001;Salido-Ruiz et al., 2019) is not possible neither. ...
In electrophysiological measurements, a recording electrode is located in an electric field and has a certainelectric potential value. Each measured signal is a potential difference between two electrodes, a measuringelectrode and a so-called reference electrode. This reference electrode is not located at infinity and outside ofany electric field, thus its electric potential is found in the measured signals. In order to isolate the local activityof the recorded structure, it is necessary to understand the relationships between the different contributors at theelectrode level and separate these different activities. In this paper, we focus on the particular setup of Behnke-Fried micro-electrodes. We propose to adapt a previous dereferencing method for separating local and distantpropagated activities taking into account the non-stationarity of the signals and the particular geometry of thesemicroelectrodes. We demonstrate, on realistically simulated signals, that the new dereferencing procedureimproves the preprocessing of these signals and might help deeper interpretation.
In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.
It is an important branch of biometrics security to obtain the cardiomagnetic signal non-invasively. In our study, the problem of sparse cardiac current source reconstruction is studied using Bayesian matching pursuit with non-Gaussian prior. The characteristics of cardiac electrical activity by source imaging are analyzed. We use goodness of fit (GoF) and root mean square error (RMSE) to measure the reconstruction ability of the source reconstruction method. The trajectory during QRS complex and T-wave segment can preliminarily reveal the conduction features of electrical excitation and effective electrophysiological information in ventricular depolarization and repolarization. The experimental results verify that our method with the source solution non-Gaussian or unknown prior is available for cardiomagnetic signal’s inverse problem and cardiac sparse source imaging.
