Channel Electrode Research Articles

The optimal electrode pore size and channel width in electrochemical flow cells

Microfluidic fuel cells, electrolyzers, and redox flow batteries utilize laminar flow channels to provide reactants, remove products and avoid their crossover. These devices often also employ porous flow-through electrodes as they offer a high surface area for the reaction and excellent mass transfer. The geometrical features of these electrodes and flow channels strongly influence energy efficiency. We derive explicit analytical relations for the optimal flow channel width and porous electrode volumetric surface area from the perspective of energy efficiency. These expressions are verified using a two-dimensional tertiary current distribution and porous electrode flow model in COMSOL and are shown to be able to predict optimal parameters in commonly used flow-through and interdigitated flow fields. The obtained analytical models can dramatically shorten modelling time and expedite the industrial design process. The optimal channel width and pore sizes we obtain, in the order of 100 microns and 1 micron respectively, are much smaller than those often used. This shows that there is a significant room for improvement of energy efficiency in flow cells that can sustain the resulting pressure drop.

LSTM-enhanced multi-view dynamical emotion graph representation for EEG signal recognition

Objective and Significance: This paper proposes an LSTM-enhanced multi-view dynamic emotion graph representation model, which not only integrates the relationship between electrode channels into electroencephalogram (EEG) signal processing to extract multi-dimensional spatial topology information but also retains abundant temporal information of EEG signals. Approach: Specifically, the proposed model mainly includes two branches: a dynamic learning of multiple graph representation information branch and a branch that could learn the time-series information with memory function. First, the preprocessed EEG signals are input into these two branches, and through the former branch, multiple graph representations suitable for EEG signals can be found dynamically, so that the graph feature representations under multiple views are mined. Through the latter branch, it can be determined which information needs to be remembered and which to be forgotten, so as to obtain effective sequence information. Then the features of the two branches are fused via the mean fusion operator to obtain richer and more discriminative EEG spatiotemporal features to improve the performance of signal recognition. Main results: Finally, extensive subject-independent experiments are conducted on SEED, SEED-IV, and Database for Emotion Analysis using Physiological Signals datasets to evaluate model performance. Results reveal the proposed method could better recognize EEG emotional signals compared to other state-of-the-art methods.

Salinity-Based Molecular Communication in Microfluidic Channels

In this work, we present a novel portable, flexible and easy-to-use experimental setup for investigating salinity-based information transmission in microfluidic channels. At the receiver, the different salinity-levels are detected by a customized electronic circuit, which measures electrical conductivity via electrodes in the microfluidic channel. We provide a detailed description of the setup, including the microfluidic chip fabrication. Moreover, we develop a rigorous mathematical model of each testbed component and an end-to-end model of the system, which we have verified through experiments. Finally, we analyzed the error performance of the setup using optimum and sub-optimum detection algorithms, such as the Viterbi algorithm and threshold detection.

EEG Emotion Recognition Method Based on 3D Feature Map and Improved DenseNet

<p>Emotion, as a high-level function of the human brain, has a great impact on people’s mental health. To fully con-sider EEG signals’ spatial information and time-frequency information, and realize human-computer interaction better. This paper proposes an improved DenseNet emotion recognition model based on 3D feature map. By extracting the differential entropy features of the θ, α, β and γ frequency bands of the EEG signals, and combining the position mapping relationship of the EEG channel electrodes, a three-dimensional feature map is constructed, and then the improved densely connected convolutional network (DenseNet) is used for secondary feature extraction and classification. To verify the effectiveness of this method, a classification experiment including positive, neutral and negative emotions is carried out on the SEED data set. The classification accuracy rates obtained in the single-subject experiment and the all-subject experiment are 98.51% and 98.68%, respectively. The experimental results show that the method of 3D feature map combined with feature reuse can get high-precision classification results, which provides a new direction for emotion recognition.</p> <p> </p>

EEG Analysis for Images Encode and Retrieval under White Noise Stimulation

Utilization of an EEG machine aids researchers in detecting the brain responses and states during provided stimulation. The EEG comes with multiple electrode channels for recording the electrical activity of the brain. This work aims to discover the brain responses under stimulation of white noise during images encoding and retrieving using the EEG machine. The motivation of this research is because limited previous works determine the association between memory encoding and retrieving process based on EEG analysis. Twenty college students participated in this experimental work. They were required to remember the images in two different conditions: control/silent and exposure to white noise with two task difficulties: easy and difficult phases. The EEG was recorded during the remembering and recalling period for data acquisition. The obtained raw EEG dataset was imported to .txt format for further processing. The Butterworth bandpass filter with a frequency range of 4 to 45 Hz was applied to filter the low and high noise components in the EEG signal. Next, the mean of de-noise EEG voltage was obtained to select the most affected channels. It was found that the Fp1, Fp2, F7, and F3 channels achieved the highest mean voltage that higher than 5 μV. Therefore, these channels were selected for extraction of relative rhythm power to investigate the effect of white noise on memory performance. The findings showed that the alpha, gamma, beta, and theta activities were highly found during the encoding and retrieving process when participants were exposed to white noise. These brain rhythms were related to participants' attentional level, information processing and calm state during the assessment. The behavioral data showed that the participants successfully remembered the images more when exposed to white noise compared to the control condition with percentage differences of 21% and 33% for easy and difficult phases due to the stochastic resonance effect and activation of brain rhythms.

Giant Spin-Valve Effect in Planar Spin Devices Using an Artificially Implemented Nanolength Mott-Insulator Region.

Developing technology to realize oxide-based nanoscale planar integrated circuits is in high demand for next-generation multifunctional electronics. Oxide circuits can have a variety of unique functions, including ferromagnetism, ferroelectricity, multiferroicity, superconductivity, and mechanical flexibility. In particular, for spin-transistor applications, the wide tunability of the physical properties due to the presence of multiple oxide phases is valuable for precise conductivity matching between the channel and ferromagnetic electrodes. This feature is essential for realistic spin-transistor operations. Here, a substantially large magnetoresistance (MR) ratio of up to ≈140% is demonstrated for planar-type (La,Sr)MnO3 (LSMO)-based spin-valve devices. This MR ratio is 10-100 times larger than the best values obtained for semiconductor-based planar devices, which have been studied over the past three decades. This structure is prepared by implementing an artificial nanolength Mott-insulator barrier region using the phase transition of metallic LSMO. The barrier height of the Mott-insulator region is only 55 meV, which enables the large MR ratio. Furthermore, a successful current modulation, which is a fundamental functionality for spin transistors, is shown. These results open up a new avenue for realizing oxide planar circuits with unique functionalities that conventional semiconductors cannot achieve.

Pattern of EEG voltage and oscillations under stimulation of Mozart’s music and white noise for visual learning process

The focus on behavioural data as a prominent indicator for evaluating the effect of stimulation on human cognitive function neglects the understanding of the body’s physiological responses. Another remain limitation is the physiological features usually analyzed from learning encode databases. Hence, the current research objectively investigates the correlation of EEG voltage and oscillations pattern from visual learning encoding and recognition stages under stimulation of various circumstances based on EEG patterns with behavioural data. The acquired EEG signal from visual assessment under no-audio and audio circumstances was processed using the Daubhecies wavelet function. The most affected EEG channels were identified according to the peak mean voltage from each electrode channel. The statistical features of mean, standard deviation, peak amplitude, and relative power were obtained from brain rhythms. The evaluation showed that the most affected EEG channel was at the Fp2 electrode with a voltage of about 0.1 V. It was related to the state of subjects that were required to identify the symbol and position of provided assessment. Most EEG features gained the highest value for recognition than the encoding stage, especially the relative power showed a statistically significant value with a p-value < 0.05. This revealed that brain oscillations were higher during recognition than encoding because the brain needed to work hard to retrieve the stored visual information. The behavioural data were parallel and in line with EEG rhythms pattern, where the highest brain oscillation led to better visual score performance. Throughout the evaluation, it was found that the EEG voltage and rhythms pattern during encoding were correlated with recognition and behavioural data.

Tonotopic fitting of the sound processor cochlear implant in normal cochlea anatomy

Introduction. Cochlear implantation is very effective due to the known tonotopic organization of the cochlea. These data are used in the distribution of signals along the channels of the cochlear implant electrode. The more accurate and natural the stimulation of the auditory nerve fibers, the better the perception of speech, sounds and speech intelligibility.The aim of the study was to compare the clinical and anatomical settings of the cochlear implant processor.Materials and methods. The study included 63 patients aged 2 to 60 years using cochlear implantation system, implants with a standard long electrode array (31.5 mm). All participants underwent computed tomography (CT) of the temporal bones with a step of 0.6 mm or less. CT data were processed using Otoplan and Sliser 3D software. The geometric dimensions of the cochlea, the length of the cochlear canal, the angular position and tonotopic frequency of each electrode were calculated, and a 3D reconstruction of the cochlea and electrode was built.Results. The correction of the frequency filters of the electrodes and the anatomical adjustment of the processor were performed according to the data obtained in the Otoplan program. A comparison was made between the clinical and anatomical setting. After the redistribution of frequency filters, patients noted a more natural sound, improved speech intelligibility. Thus, with anatomical adjustment, an exact correspondence is achieved between the central frequency of each electrode channel and the tonotopic frequency of the cochlear zone, individually determined by computed tomography.Conclusions. A new tool has appeared that allows you to significantly improve the quality and provide an individual approach to setting up processors after cochlear implantation.

All‐Transfer Electrode Interface Engineering Toward Harsh‐Environment‐Resistant MoS2 Field‐Effect Transistors (Adv. Mater. 18/2023)

MoS2 Transistors As reported in article number 2210735, Yonghuang Wu, Zeqin Xin, Kai Liu, and co-workers build harsh-environment-resistant MoS2 transistors by engineering electrode–channel interfaces. The transistors are resistant to high temperature of 350 °C, 100% relative humidity, and oxidizing environments, paving the way toward nanoscale devices working in harsh environments, for example in aerospace applications.

1-dimensional Embedding Approach for Adolescent Psychosis Prediction from EEG Brain Waves using Support Vector Machines and Deep Learning

Objective: Schizophrenia is a form of psychosis which is prevalent within the population and affects both adults and adolescents, where it has also been noted that the conventional means towards diagnosing the disorder involve a high degree of subjectivity, can yield false positive results and are overall difficult to replicate. The use of electroencephalography (EEG) brain waves has been seen to carry promise as a means towards a non-invasive diagnosis of brain states via the acquisition of the bioelectrical manifestation of neuronal activities from across the skull. The acquisition of EEG signals involves a high number of electrode channels alongside the extraction of a relatively broad number of signal features which ultimately yields a high dimensional vector that scales upwards with the addition of data from subsequent subjects. Methods: As a means around dealing with this issue, this paper proposes a combination of signal decomposition prior to feature extraction, alongside dimensionality reduction, to obtain a low dimensional representation and embedding which is sufficient to allow for an improved prediction performance with a low dimensional projection capable of distinguishing between schizophrenic and non-schizophrenic adolescents. Results: The results from the pilot carried out, showed that a combination of a metaheuristic signal decomposition, linear dimensionality reduction and the long short-term memory deep learning model provided the best classification performance for the recognition of adolescent schizophrenia from a 1-dimensional embedding representation with a prediction accuracy of 88%. Conclusion: Further work in this area would now look into the application of probabilistic modelling as a means towards inferring an associated stage of the schizophrenia psychosis where predicted to be present in an adolescent subject.

3D-assembled microneedle ion sensor-based wearable system for the transdermal monitoring of physiological ion fluctuations

Monitoring human health is of considerable significance in biomedicine. In particular, the ion concentrations in blood are important reference indicators related to many diseases. Microneedle array-based sensors have enabled promising breakthroughs in continuous health monitoring due to their minimally invasive nature. In this study, we developed a microneedle sensing-array integrated system to continuously detect subcutaneous ions to monitor human health status in real time based on a fabrication strategy for assembling planar microneedle sheets to form 3D microneedle arrays. The limitations of preparing 3D microneedle structures with multiple electrode channels were addressed by assembling planar microneedle sheets fabricated via laser micromachining; the challenges of modifying closely spaced microneedle tips into different functionalized types of electrodes were avoided. The microneedle sensing system was sufficiently sensitive for detecting real-time changes in Ca2+, K+, and Na+ concentrations, and it exhibited good detection performance. The in vivo results showed that the ion-sensing microneedle array successfully monitored the fluctuations in Ca2+, K+, and Na+ in the interstitial fluids of rats in real time. By using an integrated circuit design, we constructed the proposed microneedle sensor into a wearable integrated monitoring system. The integrated system could potentially provide information feedback for diseases related to physiological ion changes.

HAND RECOGNITION SYSTEM BASED ON ELECTROMYOGRAPHY FOR REAL-TIME HAND PROSTHETIC CONTROL

Upper limb amputation is a significant limitation for achieving routine activities. Myoelectric signals detected by electrodes well-known as Electromyography (EMG) have been targeted to control upper limb prostheses of such lost limbs. Unfortunately, the acquisition, processing and use of such myoelectric signals are sophisticated. Furthermore, it necessarily requires complex computation to fulfil accuracy, robustness, and time-consumption execution for the real-time prosthesis application. Thus, machine learning schemes for pattern recognition are a potential approach to improve the traditional control for hand prostheses due to the movement of users and muscle contraction. This paper presents real-time hand posture recognition based on three hand postures using surface EMG (sEMG) signals. sEMG signals are acquired by the electrode channel and simultaneously collected while making a hand posture. Performance evaluation relies on classification accuracy and time consumption. The performance of six real-time recognition models is evaluated which combine two projection techniques and three classifiers. Results indicate that EMG-based pattern recognition (EMG-PR) control outperforms the traditional control for hand prostheses in real-time application. The highest classification accuracy is approximately 96%, whereas the lowest time consumption is 4 ms. In addition, the accuracy is dropped when the number of electrodes decreases nearly to 3%. These outcomes can apply to real-time hand prostheses to alleviate the limited prostheses available.

High throughput particle sorting based on traveling surface acoustic wave (TSAW) is realized by coupling spiral microchannel and a novel arc electrode

Acoustofluidic technology is an ideal tool for biomedical applications. However, the sorting performance and flux of acoustofluidic chips cannot be achieved simultaneously. In this paper, we propose a method for coupling a novel arc gold interdigital transducer (IDT) with a spiral microchannel. A spiral microchannel is used to focus the target particle (20 μm) inertially to improve the sorting efficiency of the chip. The channel uses a Y-shaped inlet to bind small particles (5 μm) in a fixed stream beam to improve the sorting efficiency of the chip. The fit of arc electrode and spiral flow channel enlarges the acoustic control area, so that the target particles can still be separated effectively at high flow velocity. The results show that when the signal source frequency is 33.7 MHz and the voltage is 5 V, the designed chip can effectively separate the target particles at the flow rate of 25–65 μl min−1, and the sorting purity is 100%. The sorting efficiency decreased with the increase of flow rate, 25 μl min−1: 100 %, 35 μl min−1: 100 %, 45 μl min−1: 94 .8% (within 1.2% error), 55 μl min−1: 92 % (within 1% error), 65 μl min−1: 83 .4% (within 2.4% error). This chip provides an idea for achieving high throughput, high purity and high efficiency cell sorting.

Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.

The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs. A three-way tensor is yielded by wavelet transform of a single-trial EEG signal and decomposed into three factor matrices by a regularized canonical polyadic decomposition (CPD). The channel factor matrix is used for channel selection and the important channels are selected by calculating the correlation between channels. Regularized common spatial pattern (RCSP) is employed for feature extraction and support vector machine (SVM) for classification. The proposed TCS-RCSP algorithm was evaluated on three BCI data sets and compared with the RCSP with all channels (AC-RCSP) and the RCSP with selected channels by correlation-based channel selection method (CCS-RCSP). The results indicate that TCS-RCSP achieved significantly better overall accuracy than AC-RCSP (94.4% vs. 86.3%) with 0.01 and CCS-RCSP (94.4% vs. 90.2%) with 0.05, proving the efficacy of the proposed algorithm for classifying MI tasks.

Spatio-temporal deep forest for emotion recognition based on facial electromyography signals

Emotion recognition is a key component of human–computer interaction technology, for which facial electromyogram (fEMG) is an important physiological modality. Recently, deep-learning-based emotion recognition using fEMG signals has drawn increased attention. However, the ability of effective feature extraction and the demand of large-scale training data are two dominant factors that restrict the performance of emotion recognition. In this paper, a novel spatio-temporal deep forest (STDF) model is proposed to classify three categories of discrete emotions (neutral, sadness, and fear) using multi-channel fEMG signals.The feature extraction module fully extracts effective spatio-temporal features of fEMG signals using a combination of 2D frame sequences and multi-grained scanning. Meanwhile, a cascade forest-based classifier is designed to provide optimal structures for different scales of training data via automatically adjusting the number of cascade layers.The proposed model and five comparison methods were evaluated on our in-house fEMG dataset that included three discrete emotions and three channels of fEMG electrodes with a total of twenty-seven subjects. Experimental results demonstrate that the proposed STDF model achieves the best recognition performance with an average accuracy of 97.41%. Besides, our proposed STDF model can reduced the scale of training data to 50% while the average accuracy of emotion recognition is only reduced by about 5%. Our proposed model offers an effective solution for practical applications of fEMG-based emotion recognition.

All-Transfer Electrode Interface Engineering Toward Harsh-Environment-Resistant MoS2 Field-Effect Transistors.

Nanoscale electronic devices that can work in harsh environments are in high demand for wearable, automotive, and aerospace electronics. Clean and defect-free interfaces are of vital importance for building nanoscale harsh-environment-resistant devices. However, current nanoscale devices are subject to failure in these environments, especially at defective electrode-channel interfaces. Here, harsh-environment-resistant MoS2 transistors are developed by engineering electrode-channel interfaces with an all-transfer of van der Waals electrodes. The delivered defect-free, graphene-buffered electrodes keep the electrode-channel interfaces intact and robust. As a result, the as-fabricated MoS2 devices have reduced Schottky barrier heights, leading to a very large on-state current and high carrier mobility. More importantly, the defect-free, hydrophobic graphene buffer layer prevents metal diffusion from the electrodes to MoS2 and the intercalation of water molecules at the electrode-MoS2 interfaces. This enables high resistances of MoS2 devices with all-transfer electrodes to various harsh environments, including humid, oxidizing, and high-temperature environments, surpassing the devices with other kinds of electrodes. The work deepens the understanding of the roles of electrode-channel interfaces in nanoscale devices and provides a promising interface engineering strategy to build nanoscale harsh-environment-resistant devices.

Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals.

Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers. We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3-1 Hz, 1-2 Hz, and 2-3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques. The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2-3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment. Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.

Trapping of a Single Microparticle Using AC Dielectrophoresis Forces in a Microfluidic Chip.

Precise trap and manipulation of individual cells is a prerequisite for single-cell analysis, which has a wide range of applications in biology, chemistry, medicine, and materials. Herein, a microfluidic trapping system with a 3D electrode based on AC dielectrophoresis (DEP) technology is proposed, which can achieve the precise trapping and release of specific microparticles. The 3D electrode consists of four rectangular stereoscopic electrodes with an acute angle near the trapping chamber. It is made of Ag-PDMS material, and is the same height as the channel, which ensures the uniform DEP force will be received in the whole channel space, ensuring a better trapping effect can be achieved. The numerical simulation was conducted in terms of electrode height, angle, and channel width. Based on the simulation results, an optimal chip structure was obtained. Then, the polystyrene particles with different diameters were used as the samples to verify the effectiveness of the designed trapping system. The findings of this research will contribute to the application of cell trapping and manipulation, as well as single-cell analysis.

Portable Multi-Channel Electrochemical Device with Good Interaction and Wireless Connection for On-Site Testing.

It is very important to rapidly test the key indicators of water in the field to fully evaluate the quality of the regional water environment. However, a high-resolution measuring device that can generate small currents for low-concentration analytes in water samples is often bulky, complex to operate, and difficult for data sharing. This work introduces a portable multi-channel electrochemical device with a small volume, good interaction, and data-sharing capabilities called PMCED. The PMCED provides an easy-to-operate graphical interactive interface to conveniently set the parameters for cyclic voltammetry or a differential pulse method performed by the four electrode channels. At the same time, the device, with a current sensitivity of 100 nA V-1, was applied to the detection of water samples with high background current and achieved a high-resolution measurement at low current levels. The PMCED uses the Narrow Band Internet of Things (NB-IoT) to meet the needs for uploading data to the cloud in remote areas. The electrochemical signal preprocessing and chemometrics models run in the cloud, and the final results are visualized on a web page, providing a remote access channel for on-site testing results.

Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint.

In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.