Set Of Electrodes Research Articles

Dual-Modality 4-Terminal Electrical Capacitance and Resistance Tomography for Multiphase Flow Monitoring

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) have been successfully developed to visualise dielectric and conductive distributions respectively. However, monitoring complex multiphase flows involve imaging highly dynamic distributions, which are constantly changing between dielectric-dominant and conducive-dominant. In this work, a dual-modality ERT/ECT system is developed for imaging dielectric-dominant and conducive-dominant distributions simultaneously. With a 4-terminal measurement configuration, the system can reduce the effect of parasitic impedances, acquiring simultaneously capacitance and conductance using a single set of electrodes and a single data acquisition system.

Generation of large-volume high-pressure plasma by spatiotemporal control of space charge

Any attempt to scale pressure and volume of nonthermal plasma usually leads to instabilities due to the formation of localized space charge. The control of the plasma is limited by the discharge geometry, type of excitation, and gas composition. This article explores the possibility of controlling the space charge in a discharge with a spatially and temporally varying electric field. It is shown that a phase-staggered sinusoidal excitation to a set of conformal azimuthal electrodes in a cylindrical geometry leads to a traveling electric field. Simulations show that in space charge dominated transport, the charged species are dispersed both in the radial and azimuthal directions. This will lead to better control of the space charge and stable discharges near atmospheric pressures.

Driving voltage frequency and active electrode setup effects on ozone and UV generation of dielectric barrier discharge in air

We studied the effect of the driving voltage of frequencies 4, 7, and 10 kHz and two different active electrode setups on electrical properties, ozone production, and UV radiation of wavelengths 320–420 nm of the dielectric barrier discharge in air. The first active electrode setup comprised the strip electrode made from the copper foil. The second setup comprised the stainless steel wire mesh electrode with square cells. The ozone concentration, as well as the ozone production yield, is higher for the discharge with the strip active electrode setup than for the discharge with the mesh active electrode set up. The increased frequency decreases the voltage required to obtain maximum ozone concentration. The intensity of UV radiation is higher for the discharge with the mesh active electrode setup than for the discharge with the strip active electrode setup for all investigated frequencies. The intensity of UV radiation at a particular voltage depending on the frequency and active electrode setup sharply increases. The high concentrations of ozone produced by the discharge with the strip active electrode setup are accompanied by lower intensities of generated UV radiation. For the discharge with the mesh active electrode setup, the situation is reversed.

Optimal Selection of EEG Electrodes Using Interval Type-2 Fuzzy-Logic-Based Semiseparating Signaling Game.

This article addresses the noise contamination in spatial filtering of brain responses using a novel signaling game-based approach to the optimal selection of EEG electrodes. The proposed method takes the standard common spatial pattern (CSP) filter as an input and produces an optimal electrode set as output for effective classification of different cognitive tasks. The standard CSP algorithms are highly prone to the inclusion of noise in the EEG data and may select noisy electrodes/signal sources that are redundant for a specific cognitive task which, in turn, may lead to a lower classification accuracy. A lot of literature exists in this area of research, most of which deals with adding the regularization term in the standard CSP algorithm. However, all of these methods lack capturing the uncertainty present in the EEG responses due to intrasession and intersession variations of subjective brain response. The novelty of this article lies in designing the fuzzy signaling game-based approach for optimal electrode selection using an interval type-2 fuzzy set, which can capture both the intrasession and intersession variability of EEG responses acquired from a subject's scalp. Experiments are undertaken over a wide variety of possible cognitive task classification problems which reveal that the proposed method yields superior results in electrode selection with respect to classification accuracy. Statistical tests undertaken using the Friedman test also confirm the superiority of the proposed method over its competitors.

Enhancement in MS‐based peptide detection by microfluidic free‐flow zone electrophoresis

Matrix components are known to significantly alter the ionization of a target analyte in ESI-based measurements particularly when working with complex biological samples. This issue however may be alleviated by extracting the analyte of interest from the original sample into a relatively simple matrix compatible with ESI mass-spectrometric analysis. In this article, we report a microfluidic device that enables such extraction of small peptide molecules into an ESI-compatible solvent stream significantly improving both the sensitivity and reproducibility of the measurements. The reported device realizes this analyte extraction capability based on the free-flow zone electrophoretic fractionation process using a set of internal electrodes placed across the width of the analysis channel. Employing lateral electric fields and separation distances of 75 V/cm and 600µm, respectively, efficient extraction of the model peptide human angiotensin II was demonstrated allowing a reduction in its detection limit by one to three orders of magnitude using the ESI-MS method. The noted result was obtained in our experiments both for a relatively simple specimen comprising DNA strands and angiotensin II as well as for human serum samples spiked with the same model peptide.

Determination of the permeability of seepage flow paths in dams from self-potential measurements

The flow of the pore water in porous media generates an electrical current known as the streaming current. This current is due to the drag of the excess of charges contained in the electrical diffuse layer coating the surface of the grains. This current is associated with an electric field called the streaming potential field. The fluctuations of this field can be remotely measured using a set of non-polarizable electrodes located at the ground surface or in wells and a sensitive voltmeter. The self-potential method (SP) aims at passively measuring the streaming potential anomalies associated with ground water flow. We present a stochastic numerical framework for inverting self-potential data in order to localize seeps in dams and characterize their permeability and Darcy velocity. Our approach is based on the use of Markov chains Monte Carlo (McMC) method for solving the inverse problem. We performed first a validation of the method on a synthetic case study and then on large-scale field surveys on three different dams. Our approach is successful in localizing seeps and determining their permeability. A sensitivity study is performed on each of these three dams to better define the hydraulic and electrical parameters influencing the self-potential signal and the uncertainties associated with the estimation of those parameters. Our results show that the self-potential method can provide quantitative hydrogeological information for the characterization of seeps in dams and dikes.

Influence of Metallic Shielding on Radio Frequency Energy-Induced Heating of Leads With Straight and Helical Wires: A Numerical Case Study

Heating induced by radio frequency (RF) energy may appear in tissues near implants located in a human subject undergoing magnetic resonance imaging examination. Lead shielding was proposed to reduce such heating below dangerous levels. In this article, we employed 3-D electromagnetic (EM) and thermal co-simulations to quantify the effectiveness of metallic shielding in the presence of two large sets of nonuniform incident fields at 128 MHz. Specifically, we used a lead EM model (LEM) and computed the RF responses, i.e., the net dissipated power and net temperature increase, above background, at the electrodes. We considered a set of single electrode leads with both straight and helical wires, comparing regular unshielded configurations to different implementations of a metallic shield. The lead length, the relative permittivity of the insulator material, and the lead electrode geometry were all independently varied. For leads with helical wires we observed that: 1) the metallic shield significantly modified the LEM; 2) in most cases, the RF responses substantially increased if metallic shielding was applied; and 3) the net temperature increase in close proximity to the shield can be substantially higher than the net temperature increase in close proximity to the lead electrode. Leads with helical and straight wires exhibited significantly different behavior. Using the results obtained from generic straight wire leads to predict the results for leads with helical wires can be significantly misleading.

Effect of Sweating on Electrode-Skin Contact Impedances and Artifacts in EEG Recordings With Various Screen-Printed Ag/Agcl Electrodes

In response to the growing clinico-economic need for comprehensive home-based sleep testing, we recently developed a self-applicable facial electrode set with screen-printed Ag/AgCl electrodes. Our previous studies revealed that nocturnal sweating is a common problem, causing low-frequency artifacts in the measured electroencephalography (EEG) signals. As the electrode set is designed to be used without skin abrasion, not surprisingly this leads to relatively high electrode-skin impedances, significant impedance changes due to sweating and an increased risk of sweat artifacts. However, our recent electrochemical in vitro investigations revealed that the sweat artifact tolerance of an EEG electrode can be improved by utilizing an appropriate Ag/AgCl ink. Here we have investigated in vivo electrode-skin impedances and the quality of EEG signals and interference due to sweating in the population of 11 healthy volunteers. Commercial Ag and Ag/AgCl inks (Engineered Conductive Materials ECM LLC and PPG Industries Inc.) were used to test electrode sets with differently constructed ink layers. Electrode-skin impedances and EEG signals were recorded before and after exercise-induced sweating. There was extensive variation in the electrode-skin impedances between the volunteers and the electrode positions: 14.6–200 $\text{k}\Omega $ (PPG electrodes) and 7.7–200 $\text{k}\Omega $ (ECM electrodes). Sweating significantly decreased ( $p ) the impedances in most cases. The EEG signal quality was assessed by comparing average band powers from 0.5 to 2 Hz before and after sweating. Only slight differences existed between the ECM and PPG electrodes; however, the lowest band power ratio ( i.e . the smallest increase in the band power due to sweating) was achieved with ECM electrodes.

Trends in Decreasing Disinfection By-Products Formation during Electrochemical Technologies

Disinfecting water and wastewater electrochemically is a cost-effective and environmentally friendly alternative for the chemical disinfection. During electrochemical disinfection, the water is passed through an electrolytic cell which is equipped with a set of electrodes. The effectiveness of the process depends upon cell configuration, electrode material, electrolyte composition, microorganism, water flow rate, and current density. One of the main advantages of electrodisinfection is the on-site production of disinfectants; thereby the common drawbacks of chlorination including transportation and storage of hazardous chemicals can be avoided. On the other hand, the high cell voltages due to low electrical conductivity of water and the high capital cost are the main bottlenecks for electrodisinfection. The generation of chlorinated by-products stays the main worry related to electrochemical water treatment processes. This work discusses the main tendencies in dealing with such issues. In some setups, the electrolyte separates the anode and cathode is a proton exchange membrane. This assists to reduce the formation of perchlorate and such conduct is improved in the smallest cell for which the so short contact periods between the electrodes and the water helps to avert the formation of perchlorates when working in a single-pass mode, which becomes a really remarkable point. Other strategies are examined such as developed electrochemical advanced oxidation process, the electroperoxone (E-peroxone) process, which combines ozonation with in situ electro-generation of hydrogen peroxide (H2O2) from cathodic oxygen reduction. Electrochemical processes could be merged with nanotechnologies for better efficiency in dealing with pathogens and pollutants removal. In the next future, a hybrid process combining both techniques would be suggested as a part of treatment train for treating water and wastewater.

Экспериментальное обоснование параметров защитной электрохимической поляризации газопроводов на мерзлых грунтах

An urgent issue in the operation of Bovanenkovo-Ukhta gas pipelines in conditions of frozen and seasonally thawing soils is the refinement of parameters of anticorrosive electrochemical protection of gas pipelines. The article discusses the process of determining corrosion activity of water samples that differ in the degree of mineralization during sequential cooling to freezing using the polarization resistance method using an FT-360-40 liquid cryostat and IPC-pro potentiostat with a set of working and auxiliary electrodes.

The role of catalyst–support interactions in oxygen evolution anodes based on Co(OH)2 nanoparticles and carbon microfibers

A set of OER electrodes based on Co(OH)2 nanoparticles and carbon microfibers of tailored composition is reported, which allows extracting valuable insights on the influence of the metal-support interface in their electrocatalytic performance.

A Wearable System for Attenuating Essential Tremor Based on Peripheral Nerve Stimulation.

Objective: Currently available treatments for kinetic tremor can cause intolerable side effects or be highly invasive and expensive. Even though several studies have shown the positive effects of external feedback (i.e., electrical stimulation) for suppressing tremor, such approaches have not been fully integrated into wearable real-time feedback systems. Method: We have developed a wireless wearable stimulation system that analyzes upper limb tremor using a three-axis accelerometer and that modulates/attenuates tremor using peripheral-nerve electrical stimulation with adjustable stimulation parameters and a real-time tremor detection algorithm. We outfitted nine subjects with tremor with a wearable system and a set of surface electrodes placed on the skin overlying the radial nerve and tested the effects of stimulation with nine combinations of parameters for open- and closed-loop stimulation on tremor. To quantify the effects of the stimulation, we measured tremor movements, and analyzed the dominant tremor frequency and tremor power. Results:Baseline tremor power gradually decreased over the course of 18 stimulation trials. During the last trial, compared with the control trial, the reduction rate of tremor power was 42.17 ± 3.09%. The dominant tremor frequency could be modulated more efficiently by phase-locked closed-loop stimulation. The tremor power was equally reduced by open- and closed-loop stimulation. Conclusion: Peripheral nerve stimulation significantly affects tremor, and stimulation parameters need to be optimized to modulate tremor metrics. Clinical Impact: This preliminary study lays the foundation for future studies that will evaluate the efficacy of the proposed closed-loop peripheral nerve stimulation method in a larger group of patients with kinetic tremor.

Adaptive Emotion Detection Using the Valence-Arousal-Dominance Model and EEG Brain Rhythmic Activity Changes in Relevant Brain Lobes

Contrary to popular belief that only the frontal lobe is concerned with emotions, recent neuroscience experiments show that in each emotional state, some of the brain lobes fired clearly whereas some did not. Unlike most of the previous works, which focused on choosing a fixed set of electrodes to detect emotions, this work presents a novel approach that consists of investigating the cerebral activity while experiencing emotions to identify the brain lobes that are showing significant and relevant changes. The identification process of relevant brain lobes is adaptive as the brain activity varies from one person to another and differs from one lobe to another during the same emotional state. The electrodes of the selected brain lobes will be tuned before being used as resources to extract the features that are required for the proposed three phases classification process. Using a clustering technique, the tuning process excludes every electrode that hardly separates between relevant and irrelevant rhythmic brain changes. The results show that the proposed method outperforms all previous approaches. Compared to the best performance obtained by previous studies, the proposed method enhanced the accuracy of both valence and arousal dimensions by 5%. Besides, the accuracy of the dominance dimension was improved by 2%.

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter- and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.

A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants.

The cochlear implant (CI) is a neural prosthetic that is the standard-of-care treatment for severe-to-profound hearing loss. CIs consist of an electrode array inserted into the cochlea that electrically stimulates auditory nerve fibers to induce the sensation of hearing. Competing stimuli occur when multiple electrodes stimulate the same neural pathways. This is known to negatively impact hearing outcomes. Previous research has shown that image-processing techniques can be used to analyze the CI position in CT scans to estimate the degree of competition between electrodes based on the CI user's unique anatomy and electrode placement. The resulting data permits an algorithm or expert to select a subset of electrodes to keep active to alleviate competition. Expert selection of electrodes using this data has been shown in clinical studies to lead to significantly improved hearing outcomes for CI users. Currently, we aim to translate these techniques to a system designed for worldwide clinical use, which mandates that the selection of active electrodes be automated by robust algorithms. Previously proposed techniques produce optimal plans with only 48% success rate. In this work, we propose a new graph-based approach. We design a graph with nodes that represent electrodes and edge weights that encode competition between electrode pairs. We then find an optimal path through this graph to determine the active electrode set. Our method produces results judged by an expert to be optimal in over 95% of cases. This technique could facilitate widespread clinical translation of image-guided cochlear implant programming methods.

Functionals Aspects and Simultaneous Detection of Dopamine, Ascorbic Acid and Uric Acid Using Chitosan-Catechol Graphene and Carbon Nanotube Modified Electrodes

Dopamine functions as a neurotransmitter in the brain. The dysfunction of the dopaminergic system is the leading cause of numerous diseases such as Parkinson’s disease. Hence, it is important to find selective and sensitive detection methods for the early diagnosis of diseases related to the abnormal levels of dopamine. In this study, we show a new electrochemical sensing platform based on carbon nanotube (SWCNT) and a sheet of graphene (GRA). The novelty of our sensor is the coating of the substrates with chitosan-catechol (CC) by electrodeposition, enhancing the dopamine response by 70%. The dose-response for each set of electrodes (bare CNT, bare GRA, and coated CNT as well as coated GRA) was measured. Finally, the electrodes were tested in cerebrospinal fluid (artificial and human), for the detection of millimolar to nanomolar levels of dopamine. The electrodes exhibited high sensitivity (2.03mA mol·L-1, 1.45 mA mol·L-1, 0.0298 mA mol·L-1, and 0.0559 mA mol·L-1 for the modified CNT, bare CNT, modified GRA and bare GRA, respectively, for the oxidation of DA. The oxidation peak current was proportional to the concentration of DA in the range from 50×10-6 to 50x10-9 M (n= 6, r2 =0.98). The dopamine recovery in human CSF were, 49-78% and 65-65% with coated graphene and CNT electrodes, respectively. Our results indicate that the CC modified CNT electrodes achieved the best recovery, sensitivity, limit of detection, and selectivity compared to the uncoated CNT as well as the coated and uncoated graphene electrodes.

Optimization of Real-Time EEG Artifact Removal and Emotion Estimation for Human-Robot Interaction Applications.

Affective human-robot interaction requires lightweight software and cheap wearable devices that could further this field. However, the estimation of emotions in real-time poses a problem that has not yet been optimized. An optimization is proposed for the emotion estimation methodology including artifact removal, feature extraction, feature smoothing, and brain pattern classification. The challenge of filtering artifacts and extracting features, while reducing processing time and maintaining high accuracy results, is attempted in this work. First, two different approaches for real-time electro-oculographic artifact removal techniques are tested and compared in terms of loss of information and processing time. Second, an emotion estimation methodology is proposed based on a set of stable and meaningful features, a carefully chosen set of electrodes, and the smoothing of the feature space. The methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation on the SEED database, both under subject dependent and subject independent paradigms, to test the methodology on a discrete emotional model with three affective states.

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.

Predicting neuronal dynamics with a delayed gain control model.

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements–intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

Fullerene-Based Switching Molecular Diodes Controlled by Oriented External Electric Fields.

Employing multiscale in silico modeling, we propose switching molecular diodes on the basis of endohedral fullerenes (fullerene switching diode, FSD), encapsulated with polar molecules of general type MX (M: metal, X: nonmetal) to be used for data storage and processing. Here, we demonstrate for MX@C70 systems that the relative orientation of enclosed MX with respect to a set of electrodes connected to the system can be controlled by application of oriented external electric field(s). We suggest systems with two- and four-terminal electrodes, in which the source and drain electrodes help the current to pass through the device and help the switching between the conductive states of FSD via applied voltage. The gate electrodes then assist the switching by effectively lowering the energy barrier between local minima via stabilizing the transition state of switching process if the applied voltage between the source and drain is insufficient to switch the MX inside the fullerene. Using nonequilibrium Green's function combined with density functional theory (DFT-NEGF) computations, we further show that conductivity of the studied MX@C70 systems depends on the relative orientation of MX inside the cage with respect to the electrodes. Therefore, the orientation of the MX inside C70 can be both enforced ("written") and retrieved ("read") by applied voltage. The studied systems thus behave like voltage-sensitive switching molecular diodes, which is reminiscent of a molecular memristor.