Dry Electrodes Research Articles

Facile and universal fabrication of cellulose nanofibers from bulk lignocellulose materials and their applications in multifunctional epidermal electrophysiological signals monitoring

Cellulose nanofibers (CNF) are promising in composite functional materials, while the pulping and bleaching processes are usually needed in their production, leading to high cost and pollution. Here, a two-step liquid how water (LHW)/deep eutectic solvents (DES) pretreatment and wet mechanical grinding exfoliation strategy is employed to prepare CNF from three raw representative bulk lignocellulose materials (wheat straw, moso bamboo and poplar wood) without pulping and bleaching processes. CNF prepared from this universal approach exhibits desirable nanofibrous structure and good mechanical properties, which could serve as reinforcing and binding agents for the assembly of transition metal carbides (MXene) nanosheets to overcome their poor mechanical properties and structure stability. The obtained CNF/MXene composite film with closely stacked “mussel-like” structure exhibits enhanced mechanical properties, and demonstrates potential applications in electromagnetic interference shielding and electro-/photo-thermal heating. Furthermore, flexible and conductive CNF/MXene composite film could serve as dry electrodes to detect and monitor epidermal electrophysiological signals (electrocardiogram, electromyogram, etc.). This work provides a scalable and universal approach for the fabrication of high-performance CNF-based multifunctional materials, which is promising for the development of flexible and portable electronics.

Soft, adhesive and conductive composite for electroencephalogram signal quality improvement.

Since electroencephalogram (EEG) is a very small electrical signal from the brain, it is very vulnerable to external noise or motion artifact, making it difficult to measure. Therefore, despite the excellent convenience of dry electrodes, wet electrodes have been used. To solve this problem, self-adhesive and conductive composites using carbon nanotubes (CNTs) in adhesive polydimethylsiloxane (aPDMS), which can have the advantages of both dry and wet electrodes, have been developed by mixing them uniformly with methyl group-terminated PDMS. The CNT/aPDMS composite has a low Young's modulus, penetrates the skin well, has a high contact area, and excellent adhesion and conductivity, so the signal quality is enhanced. As a result of the EEG measurement test, although it was a dry electrode, results comparable to those of a wet electrode were obtained in terms of impedance and motion noise. It also shows excellent biocompatibility in a human fibroblast cell test and a week-long skin reaction test, so it can measure EEG with high signal quality for a long period of time.

Fabrication and Characterization of Inkjet Printed Flexible Dry ECG Electrodes

Commercial gel ECG electrodes have a limited lifetime of operation and metal dry electrodes are rigid and noisy. In this study, we developed flexible dry ECG electrodes in different shapes and sizes using an inkjet printing (IJP) technology. As an additive manufacturing technique, IJP provides the flexibility to personalize the shape and size of electrodes with rapid prototyping. IJP ECG electrodes were fabricated on flexible polyimide substrates using silver nanoparticle inks for the conductive layer and a custom polymer ink of poly(4-vinylphenol) (PVP) for the insulator layer. The maximum and minimum diameters of the electrodes were 21 and 9 mm, respectively. The thickness of IJP electrodes was 2 ± 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> . Various tests were performed for feasibility, including changing electrode position, and subject’s activity such as resting, standing, and walking using IJP ECG electrodes simultaneous with clinical standard gel electrodes, which provides an accurate comparison. Compared to the gel electrode, the circular shape IJP electrode was more promising than rectangular and square shapes as it showed more coherence to the gel electrode. IJP electrodes with different diameters were compared along with the gel electrode and the result demonstrated that the IJP electrode with a diameter of 21, 19, and 16 mm shows almost similar performance as the gel electrode. Considering ECG signal quality, IJP electrode with a diameter of 19 mm was the optimal size. These IJP dry electrodes can be usable in ECG wearable as it is flexible, reusable, and can be used for a long time.

A 3-D-Printed Portable EMG Wristband for the Quantitative Detection of Finger Motion

It is highly required to develop an affordable surface electromyography (EMG) wristband for the improved control of prostheses that is suitable to mimic human hand functions. By using the 3D-printing technology, the wristband is customizable and feasible for various arm sizes or shapes. In this paper, we have developed the 3D printed wristband for the control application. It consists of five pairs of custom-made serpentine dry electrodes, one EMG sensor, and one signal processing printed circuit board (PCB) to detect the user’s finger movements simultaneously. The wristband demonstrated its stability in EMG signal processing with a single-sensor system, which differed from other multi-sensor system devices. We verified the stability of the serpentine electrodes as bendable and stretchable. The serpentine electrodes have improved signal detection by providing conformal contact to the skin surface compared with the existing rigid dry electrodes. The flexibility allowed the electrodes to be placed in any shape of the skin surface. In a series of tests performed by the volunteer, we showed that the collected EMG signals reflected the muscle movements through signal processing. Under the muscle contraction of 75 lb, the wristband showed a signal sensitivity of 0.556 mV/lb with a 27 dB signal-to-noise ratio (SNR) for its valid EMG sensing capability. In addition, we also demonstrated the relationship between signal intensities and muscle forces at the different levels quantitatively. This work shows promising potential toward the advanced control system of prosthetic fields and its corresponding market.

3D Printed Dry Electrodes for Electrophysiological Signal Monitoring: A Review (Adv. Mater. Technol. 7/2023)

3D Printed On-Skin Electrodes In article number 2201677, Nazek El-Atab and colleagues review various 3D printing and manufacturing techniques of dry electrophysiological electrodes. This review provides a reference guide about various state-of-the-art 3D printing methodologies and materials that have been developed with a wide range of characteristics, including conductivity, flexibility, softness, adhesion, breathability, and biocompatibility with a focus on the main applications in electrocardiogram, electrooculogram, electromyogram, and electroencephalogram.

Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods (Small 17/2023)

Electromyogram-Based Lip-Reading Systems In article number 2205058, Petar M. Djurić, Shanshan Yao, and co-workers present the design of a truly natural and robust electromyogram-based lip-reading system that can capture speech-relevant lip gestures and decode lip movements for speech. The system allows for a mechanically imperceptible, skin-friendly, visually unobtrusive platform for tracking and interpreting lip movements with minimal interference.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

Mixing, Fast and Slow: Assessing the Efficiency of Electronically Conductive Networks in Hard Carbon Anodes

This work aimed to answer fundamental questions about the optimal processing and formulation of hard carbon electrodes typical of those anticipated in commercial sodium-ion cells. Procedurally simple tests were proposed to compare the effects of slurry mixing energy and conductive additives on the morphology of and conductive networks in electrodes made with hard carbons from two different manufacturers. Long-range and short-range electronic conductivity was quantified with high repeatability for samples of each hard carbon electrode produced on different days. The most significant changes induced by mixing energy were observed in the electrodes produced without conductive additives, which was found to relate to post-processing particle size. Hard carbon from one source was pulverized by high energy mixing, replacing the electronic effect of conductive additives while increasing pore tortuosity and impedance. These findings recommend evaluating the dry electrode through-resistance as a complement to quantifying pre-cycling impedance to validate mixing protocol and the application of conductive additives in hard carbon electrodes. These procedures can also serve as reliable low-cost methods for quality control at early stages of sodium-ion anode manufacturing.

Multi-Modal Portable Respiratory Rate Monitoring Device for Childhood Pneumonia Detection.

Accurate assessment of Respiratory Rate (RR) is the most important mechanism in detecting pneumonia in low-resource settings. Pneumonia is a disease with one of the highest mortality rates among young children under five. However, the diagnosis of pneumonia for infants remains challenging, especially in low- and middle-income countries (LMIC). In such situations, RR is most often measured manually with visual inspection. Accurate RR measurement requires the child to remain calm without any stress for a few minutes. The difficulty in achieving this with a sick child in a clinical environment can result in errors and misdiagnosis, even more so when the child is crying and non-cooperating around unfamiliar adults. Therefore, we propose an automated novel RR monitoring device built with textile glove and dry electrodes which can make use of the relaxed posture when the child is resting on the carer's lap. This portable system is non-invasive and made with affordable instrumentation integrated on customized textile glove. The glove has multi-modal automated RR detection mechanism that simultaneously uses bio-impedance and accelerometer data. This novel textile glove with dry electrodes can easily be worn by a parent/carer and is washable. The real-time display on a mobile app shows the raw data and the RR value, allowing a healthcare professional to monitor the results from afar. The prototype device has been tested on 10 volunteers with age variation of 3 years to 33 years, including male and female. The maximum variation of measured RR with the proposed system is ±2 compared to the traditional manual counting method. It does not create any discomfort for either the child or the carer and can be used up to 60 to 70 sessions/day before recharging.

Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

Ultrahigh loading dry-process for solvent-free lithium-ion battery electrode fabrication

The current lithium-ion battery (LIB) electrode fabrication process relies heavily on the wet coating process, which uses the environmentally harmful and toxic N-methyl-2-pyrrolidone (NMP) solvent. In addition to being unsustainable, the use of this expensive organic solvent substantially increases the cost of battery production, as it needs to be dried and recycled throughout the manufacturing process. Herein, we report an industrially viable and sustainable dry press-coating process that uses the combination of multiwalled carbon nanotubes (MWNTs) and polyvinylidene fluoride (PVDF) as a dry powder composite and etched Al foil as a current collector. Notably, the mechanical strength and performance of the fabricated LiNi0.7Co0.1Mn0.2O2 (NCM712) dry press-coated electrodes (DPCEs) far exceed those of conventional slurry-coated electrodes (SCEs) and give rise to high loading (100 mg cm−2, 17.6 mAh cm−2) with impressive specific energy and volumetric energy density of 360 Wh kg−1 and 701 Wh L−1, respectively.

Dry battery electrode processing, what's next?

Dry battery electrode processing, what's next?

3D Printed Dry Electrodes for Electrophysiological Signal Monitoring: A Review

Abstract3D printed on‐skin electrodes are of notable interest because, unlike traditional wet silver/silver chloride (Ag/AgCl) on‐skin electrodes, they can be personalized and 3D printed using a variety of materials with distinct properties such as stretchability, conformal interfaces with skin, biocompatibility, wearable comfort, and, finally, low‐cost manufacturing. Dry on‐skin electrodes, in particular, have the additional advantage of replacing electrolyte gel, which dehydrates and coagulates with prolonged use. However, issues arise in performance optimization with the recently discovered dry materials. These challenges become even more critical when the on‐skin electrodes are scaled down to a miniaturized size, making the detection of various biosignals while keeping mechanical resilience under several conditions crucial. This review paper aims to provide researchers interested in the 3D printing and manufacturing field for healthcare applications, specifically dry electrophysiological (EP) on‐skin electrodes and biosignal sensing methodologies, with a reference guide about the various state‐of‐the‐art 3D printing techniques and materials that have been developed with a focus on the main applications of EP electrodes, such as an electrocardiogram, electrooculogram, electromyogram, and electroencephalogram.

Extraction of Individual EEG Gamma Frequencies from the Responses to Click-Based Chirp-Modulated Sounds.

Activity in the gamma range is related to many sensory and cognitive processes that are impaired in neuropsychiatric conditions. Therefore, individualized measures of gamma-band activity are considered to be potential markers that reflect the state of networks within the brain. Relatively little has been studied in respect of the individual gamma frequency (IGF) parameter. The methodology for determining the IGF is not well established. In the present work, we tested the extraction of IGFs from electroencephalogram (EEG) data in two datasets where subjects received auditory stimulation consisting of clicks with varying inter-click periods, covering a 30-60 Hz range: in 80 young subjects EEG was recorded with 64 gel-based electrodes; in 33 young subjects, EEG was recorded using three active dry electrodes. IGFs were extracted from either fifteen or three electrodes in frontocentral regions by estimating the individual-specific frequency that most consistently exhibited high phase locking during the stimulation. The method showed overall high reliability of extracted IGFs for all extraction approaches; however, averaging over channels resulted in somewhat higher reliability scores. This work demonstrates that the estimation of individual gamma frequency is possible using a limited number of both the gel and dry electrodes from responses to click-based chirp-modulated sounds.

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

AbstractMultifrequency steady‐state visual evoked potentials (SSVEPs) have been developed to extend the capability of SSVEP‐based brain‐machine interfaces (BMIs) to complex applications that have large numbers of targets. Even though various multifrequency stimulation methods have been introduced, the decoding algorithms for multifrequency SSVEP are still in early development. The recently developed multifrequency canonical correlation analysis (MFCCA) was shown to be a feasible training‐free option to use in decoding multifrequency SSVEPs. However, the time complexity of MFCCA is shown to be , which will lead to long computation time as grows, where represents the input size in decoding. In this paper, a novel decoding algorithm is proposed with the aim to reduce the time complexity. This algorithm is based on linear Diophantine equation solvers and has a reduced computation cost while remaining training‐free. Our simulation results demonstrated that linear Diophantine equation (LDE) decoder run time is only one fifth of MFCCA run time under respective optimal settings on 5‐s single‐channel data. This reduced computation cost makes it easier to implement multifrequency SSVEP in real‐time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants when using dry electrode scalp electroencephalography (EEG).

Knitted ECG Electrodes in Relaxed Fitting Garments

A wide range of signal quality indices (SQIs) related to statistical methods are used to guide the optimization process of knitted textile electrodes for electrocardiogram (ECG) recordings. The electrode structure and composition as well as their integration into a garment are evaluated in view of a fully knitted garment. The dry electrodes with the best SQIs are obtained by using conductive yarn only with a compact knit structure and a medium level of roughness. The best SQIs for the e-garment were obtained by sewing electrodes at their edges only, into the knitted garment. This implementation outperforms the intarsia and double-knit method as it allows the garment some independent movement from the electrodes, reducing motion artifacts. Tests done on a healthy volunteer demonstrate excellent system performance under gentle ambulation. The advantage of using SQIs in the optimization process of dry textile ECG electrodes is that they offer a quantitative benchmark against which to compare other approaches. The fully knitted clothing allows for more relaxed e-garments when gentle ambulation is considered.

Development of New Generation Electrodes for Registration of Heart Bioelectric Potentials

When conducting electrophysiological studies, electrodes are used to register bioelectrical signals, the correct choice and use of which determine the reliability and diagnostic significance of the data obtained. Recording an electrocardiogram is a standard procedure in medicine, but its monitoring is often limited to 24 hours. This is due to the limited performance of the electrodes. The properties of the skin/electrode interface determine the performance of medical equipment. Therefore, the surface conditions and the composition of the material from which the electrode is made should comply with the requirements of the electrocardiogram recording device. It is important to implement fast transmission of a useful signal with low losses and without artifacts. Modern electrodes using Ag/AgCl have a limited-service life, since their dehydration and surface degradation lead to the formation of various artifacts on the electrocardiogram record. Alternative – dry flexible electrodes. Such electrodes can be based on carbon materials (reduced graphene oxide or a diamond-like coating) on a plastic (film) substrate. The emphasis of modern research is aimed at carrying out work on the development of dry electrodes, which would provide an opportunity to carry out high-quality long-term registration of electrocardiosignals without gels and adhesives.

Multilayer Flexible SU8-Gold Microelectrode Arrays for Wearable Bioelectronics

Wearable health trackers for vital signs monitoring are becoming ever more important especially due to the global coronavirus pandemic (COVID-19) caused by the SARS‑CoV‑2 virus which severely affect the respiratory system and can cause cardiac manifestations. Particularly, wearable solutions which can seamlessly monitor heart activity are critical to facilitate personal preventive and remote healthcare, as well as to allow early diagnosis of cardiac dysfunctions. A fundamental enabler of wearable bioelectronics is the sensing bioelectrode which is used to record surface biopotentials. While a plethora of attempts have been reported to realize skin-conformal dry electrodes and electronic skin patches, oftentimes a very critical aspect of the electrode i.e., the actual electrical interfacing of the wearable electrode to readout circuits without disturbing the skin-electrode contact, is overlooked. To address this issue, this paper reports a unique tri-layer, polymer-metal-polymer skin-conformal microelectrode design with sidewall metal coating to achieve vertical interconnect accesses (VIAs) and realize contact pads for external interfacing. The novel and optimized process flow reported herein allows repeatable fabrication of flexible electrodes in arrayed format with yields exceeding 90%. Functionality of the microfabricated electrodes were demonstrated by successful acquisition of the electrocardiogram in lead-I configuration with clear detection of the P-QRS-T complex.

Noise characteristics in spaceflight multichannel EEG.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth's surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson.

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician's computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.