Different Number Of Electrodes Research Articles

An Investigation of Microwave Tomography Technique to Image Brain Tumour Through Cross-Section Imaging with Different Number of Electrode

Brain tumours resulted from the irregular growth and cell division within the skull, indicating a highrisk for malignancies to develop and can lead to brain injury or even death. The brain tumour can affectnervoussystem’s function based on the tumour’s growth rate and location. Early detection of brain tumour is essential to improve patients’ survival rates through appropriate medical care. As the current clinical imaging has a few impediments e.g. radiation-based and expensive, tomography technique is seen possible to provide safe and inexpensive technology. The aim of this research is to investigate the feasibility of brain tumour detection using microwave tomography technique with different numbers of electrodes. The 2D finite element modelling approach is applied, and the images are reconstructed using a linear back projection (LBP) algorithm in MATLAB. A different number of rectangular sensing electrodes are arranged around the head phantom in an elliptical array, working in pairs as transmitters and receivers. The simulation shows that the system is able to detect the permittivity difference, thus detecting the existence of the tumour in the head phantom.Theimage reconstruction presented promising tumour images with an 8-antenna microwave tomography system at all locations, i.e. left, right, top, centre, and bottom, in comparison to 4-antenna and 12-antenna systems.

Analysis of Simple Algorithms for Motion Detection in Wearable Devices.

Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.

A Novel Myoelectric Control Scheme Supporting Synchronous Gesture Recognition and Muscle Force Estimation.

Aiming to provide feasible solutions for the realization of the robust and natural myoelectric control systems, a novel myoelectric control scheme supporting gesture recognition and muscle force estimation is proposed in this study. Eleven grasping gestures abstracted from daily life are selected as the target gesture set. The high-density surface electromyography (HD-sEMG) of the forearm flexor and the grasping force signal are collected simultaneously. The synchronous prediction of gesture category and instantaneous force is realized by the multi-task learning (MTL) technique. Especially, a post-processing algorithm based on threshold method is conducted to overcome the influence of force variation on the accuracy of gesture recognition. The experimental results show that the proposed post-processing method can decrease the classification error significantly. Specifically, the overall gesture classification error is reduced by 27 ~ 30 percent compared with not using the post-processing method; and 16 ~ 24 percent compared with using classical post-processing methods. The whole scheme can realize the synchronous gesture recognition and force estimation with 9.35 ± 11.48% gesture classification error and 0.1479 ± 0.0436 root-mean-square deviation force estimation accuracy. Meanwhile, it is feasible in different number of electrodes and well meets the real-time requirement of the EMG control system in response time delay (about 28.22 ~ 113.16ms on average). The proposed framework provides the possibility for myoelectric control supporting synchronous gesture recognition and force estimation, which can be extended and applied in the fields of myoelectric prosthesis and exoskeleton devices.

Design and Simulation of an 8-Lead Electrical Capacitance Tomographic System for Flow Imaging

Electrical Capacitance Tomography (ECT) is a method for determining the dielectric permittivity distribution inside an object from measurements of external capacitance. The technique differs from conventional tomographic methods in which high-resolution images are formed from slices of the material. The measuring electrodes, which are metal plates, must be large enough to give a measurable change in capacitance. The main objective of this paper is the implementation and simulation of 8 external electrode ECT systems in order to increase the quality of reconstructed permittivity images while preserving the simplicity of design and fulfilling the demand for real-time process tomography. A complete sensor model was developed to improve the accuracy of the forward validation, especially the validation of measured data from neighboring electrodes. A prototype ECT sensor with high sensitivity was designed that can be applied to all materials which have low electrical conductivity. The capacitance between different electrode pairs is calculated for some typical permittivity distributions based on LabVIEW and MATLAB. The obtained capacitance data can be used to reconstruct images. The sensitivity distributions for the ECT sensors with different numbers of electrodes were analyzed. Preliminary tests were performed and the developed prototype showed good performance. The developed concept contributes to the study and comprehension of the ECT systems that can be used for the monitoring of oil-gas flow.

Spatial-Temporal Neural Network for P300 Detection

P300 spellers are common brain-computer interface (BCI) systems designed to transfer information between human brains and computers. In most P300 detections, the P300 signals are collected by averaging multiple electroencephalographic (EEG) changes to the same target stimuli, so the participants are obliged to endure multiple repeated stimuli. In this study, a spatial-temporal neural network (STNN) based on deep learning (DL) is proposed for P300 detection. It detects P300 signals by combining the outputs from a temporal unit and a spatial unit. The temporal unit is a flexible framework consisting of several temporal modules designed for analyzing brain potential changes in the time domain. The spatial unit combines one-dimensional convolutions (Conv1Ds) and linear layers to generalize P300 features from the space domain, and it can decode EEG signals recorded using different numbers of electrodes. Both amyotrophic lateral sclerosis (ALS) patients and healthy subjects can benefit from this study. In the within-subject P300 detection and the cross-subject P300 detection, our approach gained higher performance with fewer repeated stimuli than other comparative approaches. Furthermore, we applied the proposed STNN in the P300 detection challenge of BCI Competition III. The accuracy score was 89% in the fifth round of repeated stimuli, outperforming the best result in the literature (accuracy = 80%) to the best of our knowledge. The results demonstrate that the proposed STNN performs well with limited stimuli and is robust enough for various P300 detections.

Investigation of Spatial Resolution of Electrical Capacitance Tomography Based on Coupling Simulation

Electrical capacitance tomography (ECT) has been developed for imaging permittivity distribution of two-phase flows in a pipe or vessel with high temporal resolution. The spatial resolution of ECT is relatively low due to its soft-field, ill-posed, and ill-conditioned nature. So far, there is no comprehensive analysis of the spatial resolution of ECT for complex two-phase flows. To assess the merits of ECT systematically, 2-D modeling is carried out based on fluid-field and electrostatic-field coupling simulation for liquid–solids two-phase flows. The real permittivity distribution and the corresponding capacitance measurements can be obtained simultaneously. The effect of the number of electrodes, the mesh used for reconstruction, and the initialization method for the Landweber iteration algorithm on image quality are investigated. In terms of the correlation coefficient between the reconstructed permittivity distribution and the real permittivity distribution obtained from the coupling simulation model, the accuracy and the stability of reconstructed images are quantitatively evaluated. Furthermore, the sensitivity matrices and point spread functions calculated without capacitance measurements are used to compare the performance of ECT sensors and validate the results with capacitance measurements from the coupling simulation. The two methods, with and without capacitance measurements, are used to evaluate the change in spatial resolution of ECT with a different number of electrodes.

Design of Wearable EEG Devices Specialized for Passive Brain-Computer Interface Applications.

Owing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate emotional or attentional states. Emotion-specialized EEG headsets were designed to maximize the accuracy of classification of different emotional states using the emotion-associated EEG dataset, and attention-specialized EEG headsets were designed to maximize the temporal correlation between the EEG index and the behavioral attention index. General purpose electrode configurations were designed to maximize the overall performance in both applications for different numbers of electrodes (2, 4, 6, and 8). The performance was then compared with that of existing wearable EEG devices. Simulations indicated that the proposed electrode configurations allowed for more accurate estimation of the users’ emotional and attentional states than the conventional electrode configurations, suggesting that wearable EEG devices should be designed according to the well-established EEG datasets associated with the target pBCI applications.

Dimensionality Transcending: A Method for Merging BCI Datasets With Different Dimensionalities.

We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (BCI). Our proposal uses a two-step procedure that transforms the data points so that they become matched in terms of dimensionality and statistical distribution. In the dimensionality matching step, we use isometric transformations to map each dataset into a common space without changing their geometric structures. The statistical matching is done using a domain adaptation technique adapted for the intrinsic geometry of the space where the datasets are defined. We illustrate our proposal on time series obtained from BCI systems with different experimental setups (e.g., different number of electrodes, different placement of electrodes). The results show that the proposed method can be used to transfer discriminative information between BCI recordings that, in principle, would be incompatible. Such findings pave the way to a new generation of BCI systems capable of reusing information and learning from several sources of data despite differences in their electrodes positioning.

Optimal Design of the Vertical Earthing with Electrodes Arranged in Line

The design methods of earthing from standards recommend the choice of electrode lengths and propose that the distances between electrodes to be 1–3 times larger than their length. The number of electrodes is determined from the condition of achieving the design earth resistance, while the design ends with the choice of one of the variants. This paper presents the methodology for calculating the earthing system with cylindrical, vertical electrodes arranged in a line. The main variables are the length and the number of earth electrodes, as well as the distance between adjacent ones. Firstly, a set of technologically advantageous values for the earth electrode length is established (e.g., 10 values). For each value of the electrode length and different numbers of electrodes (e.g., 11 values), the distance between adjacent electrodes is determined (e.g., for 110 cases), which leads to the design value resistance. Finally, optimal solutions are identified based on the five optimal applied criteria. The proposed optimal criteria for earthing design are the footprint area, the total earthing volume, the total dispersion surface, the total metal mass, and the investment costs. Comparing the optimal solutions with other technically possible solutions clearly highlights substantial savings concerning space, material, and cost.

Feasibility of Electroacoustic Tomography: A Simulation Study.

The feasibility of electroacoustic tomography (EAT) was investigated for in situ monitoring the electric field distribution in soft tissue. EAT exploits the phenomenon that the amplitude of acoustic emission generated by an electric field is proportional to the electrical energy deposition in tissue. After detecting these acoustic waves with ultrasound transducers, an image of the electric field distribution can be reconstructed in real-time. In our computer simulations, the electric field distribution in soft tissue was generated by solving general partial differential equations (PDEs) using finite element analysis (FEA). The electric field distributions were converted into initial pressure distributions, and the propagation of the induced acoustic waves was simulated using K-Wave simulation. A circular array of 128 ultrasound transducers was placed around the target to detect the acoustic waves, and a time reversal reconstruction algorithm was used to reconstruct the EAT image. A different number of electrodes set at different distances with different voltage inputs on the electrodes were performed to simulate different electric field distributions during electroporation. It was found that the electrical energy deposition in reconstructed EAT imaging is decreased as the distance of the electrodes increases. We also have investigated the sensitivity of the EAT imaging with different voltage inputs. The minimal voltage we can detect with EAT is 970 V at the pulsewidth of 180 ns. The results of this study demonstrated that EAT is a feasible technique for monitoring the electric field distribution and guiding the electrotherapy in future clinical practice.

Proportional–Integral Controller Modified Landweber Iterative Method for Image Reconstruction in Electrical Capacitance Tomography

While Landweber iteration is a popular image reconstruction algorithm for electrical capacitance tomography (ECT), it suffers from semi-convergence. In process control, PID controllers are commonly used, where P stands for proportional, I stands for integral, and D stands for derivative. Proper setting of the PID parameters can reduce the response time and enhance the accuracy and stability of control systems. In this paper, an extended PI controller is integrated with the Landweber method to relieve its semi-convergence, which is named the Landweber-PI method. To stabilize the iteration process, constraints are imposed on the integral term of the PI controller. In comparison with the conventional Landweber method and its modified variants, simulation was conducted to verify the effectiveness of the proposed method by imaging typical distributions using ECT sensors with different number of electrodes, 8 or 12 or 16 electrodes, and capacitance data without and with noise. The simulation results were validated by static experiment, showing that the proposed method converges within 50 iterations for the specified core, annular, stratified, and other two complex distributions and remains stable after 1000 iterations, while the other methods converge slowly and become divergent eventually. Regarding the reconstruction accuracy, the derived void fractions by the proposed method have smaller absolute deviations than the other methods in most cases, with a minimum of 0.3% and a maximum of 4.3%. The gas–solid flows in a real fluidized bed were imaged, with more image details reconstructed after five iterations. The improved performance of the Landweber-PI method was justified by singular value decomposition analysis.

Performance Analysis of SAW Gas Sensors with Different Number of Electrodes

SAW technology is used to detect presence of certain molecules as used for gas sensing. Different geometries are designed with variation in number of interdigitated electrodes. Designed device used the layers of materials with variation of depth. FEM analysis is performed for two different structures. Shift in resonant frequency is observed as performance measuring parameter. Deformed shape plots are obtained with electric potential distributions for different eigenfrequencies. Different simulation results are compared to get highly sensitive device. Applications are found in many applications to sense micro, nano particles etc.

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.

Adaptive Selection of Relaxation Factor in Landweber Iterative Algorithm

It is crucial to select a suitable relaxation factor in Landweber iterative algorithm for electrical capacitance tomography, because it affects the convergence and convergence rate. Previous study shows that the relaxation factor should be selected adaptively according to the sensor structure (e.g., the number of electrodes), permittivity distribution, and noise level in capacitance data. With different number of electrodes and four typical permittivity distributions, the relaxation factor and the related convergence are investigated in consideration of the change in relative image error and relative capacitance residual. By adding noises with different levels to noise-free data, their influences on the selection of relaxation factor and convergence are characterized. For a typical permittivity distribution, the corresponding relaxation factor is selected based on the upper bound of all relaxation factors, which are determined by a sensor design. The performance of Landweber algorithm with an adaptively selected relaxation factor is compared with constant relaxation factors and updated relaxation factor, showing that the proposed method can ensure convergence with less computation time than other relaxation factors.

Microparticle dynamics in Coulomb structures in linear electrodynamic traps with different numbers of electrodes

The microparticle dynamics in linear electrodynamic trap with different numbers of electrodes (8, 12 and 16) was theoretically studied. The regions of particle confinement as the dependencies of frequency of alternating voltage on particle charges and number of electrodes were determined. Amplitudes of particle oscillations decreases with the growth of the frequency as well as with the growth of the number of trap electrodes.

Characterization of a multi-plane electrical capacitance tomography sensor with different numbers of electrodes

Choosing the number of electrodes is crucial for a multi-plane electrical capacitance tomography (ECT) sensor to obtain satisfactory capacitance data and good image quality. In this paper, issues with the number of electrodes in three-plane ECT sensors are discussed. The number of electrodes on each plane for the three sensors is 4, 8, and 12, respectively. Three quality measures of resolution matrix, i.e. the amplitude response, relative position error, and spatial resolution, are defined and used for evaluating the preliminary performance of ECT sensors. Based on numerical simulation, the noise-free and noisy capacitance data obtained from the three sensors are used for image reconstruction by linear back projection (LBP) and Landweber iteration algorithms. The image quality is evaluated quantitatively in terms of image error and correlation coefficient. Based on static experiment, the effect of the number of electrodes on the quality of the capacitance data and reconstructed images is also analyzed. According to the numerical simulation and experiment results, the selection of the number of electrodes under different conditions is suggested.

Comparison of body fat using various bioelectrical impedance analyzers in university students

Background: At present, the portfolio of devices using the bioelectrical impedance (BIA) method is continuously expanding as a result of the wide use of this method in the field as measurements by this method are fast and staff training is simple and reasonably priced. Nonetheless, the problem is that despite using the same method, bioimpedance analyzers can differ in many parameters. They use different electric current frequencies, a different number of electrodes and the electric current may be conducted through different parts of the body.Objective: The main objective of the study is to compare and evaluate the differences of values of the analysis of the body fat of university students measured by BIA analyzers that differ in the applied electric current frequency, number of electrodes and flow of the electric current through the individual body parts.Methods: The research included 125 participants (70 male and 55 female). The measurements were taken by the following analyzers: Tanita 418 MA, InBody 720, InBody R20 and Omron BF 300.Results: The differences in the mean values of the body fat representation between the used analyzers in men ranged from 0.1 to 3.4% and from 0.0 to 2.4 kg, in women from 0.5 to 6.5% and from 0.4 to 3.8 kg in relation to the used analyzer.Conclusions: In men with regular physical activity, the values measured by InBody R20 were statistically and practically different. The analyzer measured higher values that other analyzers. In women, there were statistically and practically significant differences in the values measured by Omron BF 300. This analyzer measured lower values than other analyzers.

Twin-array capacitance sensor for multi-parameter measurements of multiphase flow

We propose a twin-array capacitance (TAC) sensor for the measurement of concentration, velocity, and flowrate of gas–solid two-phase flow. Using the sensitivity non-uniformity of a neighboring electrode, the regional concentration of the cross-section was reconstructed directly. Additionally, the finite element method was used to analyze the capacitance of the sensors composed of a different number of electrodes. TAC sensors with 4, 6, and 8 electrodes were found to be the best for regional concentration measurements. Based on this, the 8-electrode twin-plane electrical capacitance tomography (ECT) sensor, the 4-electrode TAC sensor, and the 6-electrode TAC sensor were used to measure the concentration, velocity, and flowrate of granules in granular flow. The flowrates measured by ECT and TAC were compared with the flowrate obtained by a gravity sensor to verify the measurement accuracy. Experiments on vertical and inclined pipelines with granular flow were carried out. We found that the flowrate accuracy of the 4-electrode TAC is distinctly better than that of the 6-electrode TAC in the vertical pipeline while the flowrate accuracy of the 4-electrode TAC and the 6-electrode TAC were similar for the inclined pipeline.

Prediction of Hand Trajectory from Electrocorticography Signals in Primary Motor Cortex

Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus in recent years. Studies using ECoG have come out with success in such endeavors as classification of arm movements and natural grasp types, regression of arm trajectories in two and three dimensions, estimation of muscle activity time series and so on. However, there still remains considerable work to be done before a high performance ECoG-based neural prosthetic can be realized. In this study, we proposed an algorithm to decode hand trajectory from 15 and 32 channel ECoG signals recorded from primary motor cortex (M1) in two primates. To determine the most effective areas for prediction, we applied two electrode selection methods, one based on position relative to the central sulcus (CS) and another based on the electrodes' individual prediction performance. The best coefficients of determination for decoding hand trajectory in the two monkeys were 0.4815±0.0167 and 0.7780±0.0164. Performance results from individual ECoG electrodes showed that those with higher performance were concentrated at the lateral areas and areas close to the CS. The results of prediction according with different numbers of electrodes based on proposed methods were also shown and discussed. These results also suggest that superior decoding performance can be achieved from a group of effective ECoG signals rather than an entire ECoG array.

Evaluation of effect of number of electrodes in ECT sensors on image quality

It is commonly assumed that the use of more electrodes in an electrical capacitance tomography (ECT) sensor would result in higher resolution images. In this paper, the issues with the number of electrodes will be discussed. To investigate the effect of the number of electrodes on the quality of reconstructed images, ECT sensors with different number of electrodes-4, 8, 12, 16, 20, 24, and 32-are investigated. The capacitance between different electrode pairs is calculated for some typical permittivity distributions using a finite element method. The obtained capacitance data are then used to reconstruct images using the projected Landweber iteration algorithm based on both linear and semi-linear ECT models. The sensitivity distributions for the ECT sensors with different number of electrodes are analyzed. The main conclusion is that limited new information can be obtained and little improvement in the quality of images be achieved if the number of electrodes is more than 12.