Search Results (145)

Environmental monitoring across extensive regions is often constrained by the high costs of conventional laboratory analysis. This study proposes a methodology for electrophysiological characterization of Aloe vera as a potential biological dosimeter for low-cost environmental sensing. Using an ATMega328P-based acquisition system with high-input-impedance signal conditioning, we recorded plant biopotentials under controlled abiotic stressors. Signal variations were evaluated as a function of leaf morphology, electrode placement, and environmental variables, including light intensity, soil moisture, water saturation, and pH. The statistical validation included Jaccard similarity coefficients for repeatability and Kruskal–Wallis tests for group comparisons. The measurements showed highly repeatable baseline behavior (Jaccard similarity in the range 0.95–0.99) and significant differences across stress conditions, particularly under changes in light intensity. These findings support the feasibility of using Aloe vera electrophysiological signals as a robust and low-cost basis for developing plant-based biosensing approaches in environmental monitoring applications. Full article

Electric impedance spectrum (EIS) is attracting attention in many areas of science, ranging from electrochemistry and material science to medical diagnosis. Interestingly, theoretical description often stops at material constants and specific physical mechanisms are represented by equivalent circuit elements, which is also motivated by the common use of various bridge methods. This specifically applies to biological samples, which exhibit a rich variety of responses to the electric field. Here, we present a step further from the description that utilizes equivalent circuit elements. We demonstrate how alteration of the mesoscopic structure affects the EIS in a biological sample: a cucumber under thermal treatment that comprises a cooling and warming phase. As the freezing temperature of water is exceeded during the cycle, the cucumber becomes frosted, which leads to unrecoverable changes in the internal structure, with no change of chemical composition. The experimental evidence is complemented by theoretical analysis, based on a novel approach to modeling non-stationary problems, derived from the stationary Poisson–Boltzmann equation. We demonstrate a qualitative agreement between the theoretical and the experimental results, and discuss the procedure for tuning the model. We also demonstrate that, of the temperature variations of the position of the beta dispersion, the one related to the mesoscopic structure, can be used to assess the ionic strength of the material, determine the microscopic diffusion constant, or reflect the changes in mesoscopic structure, depending on experimental protocol. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research. Full article

Introduction: Today, scientists are searching for alternative approaches to preventing metabolic diseases, particularly non-alcoholic fatty liver disease, which reduces the healthy life expectancy of the working population. Fungi, such as Hericium coralloides (Scop.) Pers., are promising raw materials for extracting bioactive substances with preventative potential. Materials and Methods: This review covered review and research articles published over the last 42 years and indexed in the databases of the eLIBRARY.RU, the National Center for Biotechnology Information, and Scopus. Results and Discussion: It has been established that H. coralloides is valued for its nutritional properties due to its rich protein, fat, and mineral composition. It is in demand for pharmaceutical purposes due to its content of bioactive metabolites. The most studied metabolites are lovastatin and ergothioneine. The activity of these biologically active substances against NAFLD has been confirmed by studies in vitro and in vivo. Market analysis revealed that most dietary supplements contain fungal mycelium or its extract. It is preferable to use pure metabolites of H. coralloides as nutrients in dietary supplements and functional foods, since it allows the scientists to standardize their doses, target the therapeutic effect (immunity, neuroprotection, or antitumor), and reduce the required intake of the product. Since this fungus is a rare species in nature, its biomass should be grown in vitro for industrial use. Conclusions: Further research will focus on developing methods for extracting H. coralloides metabolites and assessing their biopotential in vivo and clinical studies. Full article

Background: Automated pain assessment aims to enable objective measurement of patients’ individual pain experiences for improving health care and conserving medical staff. This is particularly important for patients with a disability to communicate caused by mental impairments, unconsciousness, or infantile restrictions. When operating in the critical domain of health care, where wrong decisions harbor the risk of reducing patients’ quality of life—or even result in life-threatening conditions—multimodal pain assessment systems are the preferred choice to facilitate robust decision-making and to maximize resilience against partial sensor outages. Methods: Hence, we propose the MultiModal Supervised Contrastive Adversarial AutoEncoder (MM-SCAAE) pretraining framework for multi-sensor information fusion. Specifically, we implement an application-specific model to accomplish the task of pain recognition using biopotentials from the publicly available heat pain database BioVid. Results: Our model reaches new state-of-the-art performance for multimodal classification regarding all pain recognition tasks of ‘no pain’ versus ‘pain intensity’. For the most relevant task of ‘no pain’ versus ‘highest pain’, we achieve $84.22\%$ accuracy ($F_1$-score: $83.72\%$), which can be boosted in practice to an accuracy of ≈$95\%$ through grouped-prediction estimates. Conclusions: The generic MM-SCAAE framework offers promising perspectives for multimodal representation learning. Full article

Ageing results in the progressive loss of near vision, known as presbyopia, which impacts individuals and society. Existing corrective methods offer only partial compensation and do not restore dynamic focusing at varying distances. This work presents a closed-loop correction system for presbyopia, employing biopotential signals from the ciliary muscle and an artificial neural network to predict the eye’s accommodative state in real time. Non-invasive contact lens electrodes collect biopotential data, which are preprocessed and classified using a multi-layer perceptron. The classifier output guides a control system that adjusts an external focus-tunable lens, enabling both accommodation and disaccommodation similar to a young eye. The system demonstrated an accuracy of 0.79, with F1-scores of 0.78 for prediction of accommodation and 0.77 for disaccommodation. Using the system in two presbyopic subjects, near visual acuity improved from 0.28 and 0.38 to 0.04 and −0.03 logMAR, while distance acuity remained stable. Despite challenges such as signal quality and individual variability, the findings demonstrate the feasibility of restoring near-natural accommodation in presbyopia using neuromuscular signals and adaptive lens control. Future research will focus on system validation, expanding the dataset, and pre-clinical testing in implantable devices. Full article

This work presents the development and characterization of a flexible capacitive electrode for non-contact ECG acquisition, fabricated using a simple and cost-effective method from readily available materials. The electrode consists of a multilayer structure with a copper conductor laminated by a polyimide (Kapton^®) dielectric layer on a polyurethane support. The impedance and capacitance of the electrode were evaluated under varying textile moisture levels with artificial sweat, as well as after exposure to common disinfectants including ethyl alcohol and iodine tincture. Electrochemical impedance spectroscopy (EIS) and broadband impedance measurements (10⁻¹–10⁵ Hz) confirmed stable capacitive behavior, moderate sensitivity to moisture, and chemical stability of the Kapton–copper interface under conditions simulating repeated use. A custom front-end readout circuit was implemented to demonstrate through-textile ECG signal acquisition. Simulator tests reproduced characteristic waveform patterns, and preliminary volunteer recordings confirmed the feasibility of through-textile acquisition. These results highlight the promise of the electrode as a low-cost platform for future wearable biosignal monitoring technical research. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

The current study investigates the chemical profiling, antioxidant activities, and enzyme inhibitory and cytotoxic potential of the water and methanolic extracts of different parts (flower, leaf, and bulb) of Muscari armeniacum. Chemical profiling was performed using UHPLC-MS/MS. At the same time, different in vitro assays were employed to support the results for antioxidant potential, such as DPPH, ABTS, FRAP, CUPRAC, metal chelation, and PBD, along with the measurement of total phenolic and flavonoid contents. Enzyme inhibition was investigated for cholinesterase (AChE and BChE), α-amylase, α-glucosidase, and tyrosinase enzymes. Additionally, the relative expression of NRF2, HMOX1, and YGS was evaluated by qPCR. LC-MS/MS analysis indicated the presence of some significant compounds, including apigenin, muscaroside, hyacinthacine A, B, and C, and luteolin. According to the results, the highest TPC and TFC were obtained with both extracts of the leaves, followed by the water extract (flower) and methanolic extract of the bulb. In contrast, the methanolic extract from the bulb exhibited the highest antioxidant potential using DPPH, ABTS, CUPRAC, and FRAP, followed by the extracts of leaves. In contrast, the leaf extracts had the highest values for the PBD assay and maximum chelation ability compared to other tested extracts. According to the enzyme inhibition studies, the methanolic extract from the bulb appeared to be the most potent inhibitor for all the tested enzymes, with the highest values obtained for AChE (1.96 ± 0.05), BChE (2.19 ± 0.33), α-amylase (0.56 ± 0.02), α-glucosidase (2.32 ± 0.01), and tyrosinase (57.19 ± 0.87). Interestingly, the water extract from the bulb did not inhibit most of the tested enzymes. The relative expression of NRF2 based on qPCR analysis was considerably greater in the flower methanol extract compared to the other extracts (p < 0.05). The relative expression of HMOX1 was stable in all the extracts, whereas YGS expression remained stable in all the treatments and had no statistical differences. The current results indicate that the components of M. armeniacum (leaves, flowers, and bulb) may be a useful source of natural bioactive compounds that are effective against oxidative stress-related conditions, including hyperglycemia, skin disorders, and neurodegenerative diseases. Complementary in silico approaches, including molecular docking, dynamics simulations, and transcription factor (TF) network analysis for NFE2L2, supported the experimental findings and suggested possible multi-target interactions for the selected compounds. Full article

This paper presents a novel, low-cost, portable impedance analyzer system designed for biopotential electrode evaluation and skin/electrode impedance measurement, critical for enhancing bioelectrical signal quality in healthcare applications. In contrast with conventional systems that depend on external PCs or host devices for data acquisition, visualization, and analysis, this design integrates all functionalities into a single, compact platform powered by the Analog Devices AD5933 impedance converter and a Raspberry Pi 4. The design incorporates custom analog circuitry to extend the measurement range from 10 Hz to 100 kHz and supports a wide impedance spectrum through switchable feedback resistors. Validated against a benchtop impedance analyzer, the system demonstrates high accuracy with normalized root-mean-square errors (NRMSEs) of 1.41% and 3.77% for the impedance magnitude and phase of passive components, respectively, and 1.43% and 1.29% for the biopotential electrode evaluation and skin/electrode impedance measurement. This cost-effective solution, with a total cost of USD 159, addresses the accessibility challenges faced by smaller research labs and healthcare facilities, offering a compact, low-power platform for reliable impedance analysis in biomedical applications. Full article

Commercial wearable systems for surface electromyography (sEMG) acquisition often trade bandwidth, synchronization, and battery life for miniaturization, and their proprietary designs inhibit reproducibility and cost-effective customization. To address these limitations, we developed MOT, a fully wireless, multichannel platform built from commodity components that can be replicated in academic laboratories. Each sensor node integrates an AD8232 analog front-end configured for 19–690 Hz bandwidth (59 dB mid-band gain) with a 12-bit successive approximation ADC sampling at 1 kS/s. Packets of 120 samples are broadcast via the low-latency ESP-NOW 2.45 GHz protocol to a central hub, which timestamps and streams data to a host PC over USB-UART. Bench tests confirmed the analog response and showed mains interference at least 40 dB below voluntary contraction levels; the cumulative packet loss remained below 0.5% for six simultaneous channels at 100 m line-of-sight, with end-to-end latency under 3 ms. A 180 mAh Li-ion cell was used to power each node for 1.8 h of continuous operation at 100 mA average draw, and the complete sensor, including enclosure, was found to weigh 22 g. MOT reduced a 60 Hz artifact magnitude by up to 22 dB while preserving signal bandwidth. The hardware, therefore, provides a compact and economical solution for biomechanics, rehabilitation, and human–machine interface research that demands mobile, high-fidelity sEMG acquisition. Full article

Non-contact electrodes have garnered significant attention as an alternative non-invasive biopotential measurement method that offers advantages such as improved subject comfort and ease of integration into everyday environments. Despite these benefits, ensuring consistent signal quality over time remains a critical challenge, particularly in applications like electrocardiography (ECG), where accuracy and reliability are paramount. This study investigates the temporal stability of signal quality in non-contact biopotential electrodes, with a primary focus on ECG monitoring. Our measurements showed a significant change in the recorded signal quality during prolonged measurement periods, which impacts the integrity and reliability of the measurements. Furthermore, it significantly impacts any shorter (<10 min) consecutive measurements of influential parameters (such as properties of electrodes, dielectric, etc.) since it removes the crucial ceteris paribus principle: the signal may not change just due to the change in influential parameters, but also due to the passage of time. Through a series of controlled experiments, we analyze how factors such as temperature, pressure on the electrodes, and humidity influence signal quality over extended durations (10 min or more). The results demonstrate key insights into the temporal dynamics of non-contact electrode performance, identifying potential sources of signal degradation and avenues for mitigation. Full article

The measurement of electrical potentials in the human body is becoming increasingly important in healthcare as a valuable diagnostic parameter. In ophthalmology, while these signals are primarily used to assess retinal function, other applications, such as recording accommodation-related biopotentials from the ciliary muscle, remain poorly understood. Here, we present the development and evaluation of a novel implantable ring electrode for recording biopotentials from the ciliary muscle. Inspired by capsular tension rings, the electrode was fabricated using laser cutting, wiring, and physical vapor deposition coating. The constant impedance and weight over a simulated aging period of 391 days, demonstrated the electrode’s stability. In vivo testing in non-human primates further validated the electrode’s surgical handling and long-term stability, with no delamination or tissue ingrowth after 100 days of implantation. Recorded biopotentials from the ciliary muscle (up to 700 µV) exceeded amplitudes reported in the literature. While the results are promising, further research is needed to investigate the signal quality and origin as well as the correlation between these signals and ciliary muscle activity. Ultimately, this electrode will be used in an implanted device to record ciliary muscle biopotentials to control an artificial lens designed to restore accommodation in individuals with presbyopia. Full article

Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a −3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μ${\mathrm{V}}_{\mathrm{rms}}$, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μ$m^2$, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency. Full article