Recording Of Signals Research Articles

The Respiratory-Swallow Coordination may be Related to Aspiration in Infratentorial Stroke Patients.

To investigate the characteristics of respiratory-swallow coordination (RSC) in poststroke dysphagia (PSD) patients following infratentorial stroke (IS) and further explore their association with aspiration. PSD patients after IS and age-matched healthy controls were recruited. Nasal airflow and sound signals were recorded using a nasal cannula-type flow sensor and contact microphone, which were synchronized with videofluoroscopic swallowing studies (VFSS). For healthy controls, only nasal airflow and sound signals recordings were conducted. A 5ml thickened liquid was utilized during these assessments. The penetration-aspiration scale (PAS) score was determined through VFSS. Various parameters, including swallowing apnoea duration (SAD), swallowing duration (SD), swallowing latency duration (SLD), number of swallows (NS), and RSC patterns, were analysed to assess RSC. A total of 37 patients with PSD following IS-comprising 25 non-aspirators and 12 aspirators-and 31 age-matched healthy controls were included. The PSD patient group exhibited a shorter SAD (p = 0.016), a longer SD (p = 0.000), and fewer NS (p = 0.000) compared to the healthy control group. Among the PSD patients, those who aspirated exhibited a notably shorter SAD (p = 0.018) and longer SD (p = 0.028) compared to non-aspirators. The prevalence of the swallow-inspiration pattern was higher in PSD patients (p = 0.006), particularly among those who aspirated (p = 0.010). Logistic regression analysis and the area under the receiver operating characteristic curve (AUC) indicated that both SAD (AUC = 0.825, p = 0.002) and SD (AUC = 0.757, p = 0.020) were significant predictors of aspiration. The optimal cut-off values for SAD and SD were determined to be 0.19s and 1.93s, respectively. The RSC characteristics in patients with PSD following IS differed from those observed in healthy controls, particularly among patients who experienced aspiration. In these patients, a shorter SAD and longer SD may contribute to an increased risk of aspiration.

Recent progress of hydrogels in brain-machine interface

The long-term stable monitoring of brain signals, including electroencephalogram (EEG), electrocorticogram (ECoG) and local field potential (LFP), is of great significance for the fundamental research in brain science, artificial intelligence and the diagnosis and treatment of brain-related disorders. Therefore, both non-invasive and invasive brain-machine interfaces based on different materials and structures have been widely studied due to their unique performance. Among these materials, hydrogels have emerged as a promising interface material for brain signal collection systems due to their similar mechanical properties to biological tissues, excellent biocompatibility, strong self-adhesive properties, and exceptional ionic conductive characteristics. This review aims to provide an overview on the recent progress of hydrogel-based brain interfaces in the recording of brain signals with non-invasive and invasive methods. It is expected that this paper will serve as a valuable summary and reference for future research in the hydrogel-based brain interface.

CONSTRUCTION OF EARTH CRUST SECTIONS DOWN TO THE MOHO BOUNDARY BASED ON LOW AMPLITUDE REFLECTED WAVES OF RIVER SEISMIC SURVEY USING CPD-2D METHOD (Vitim River, junction zone of the Angara-Lena monocline of the Siberian platform and the Bodaibo-Patom folded system)

In 2019, employees of the Research and Production Enterprise Luch jointly with specialists of the Seismological Branch of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) carried out river seismic exploration works using the common depth point method (CDP-2D) along a 170 km long profile in the lower reaches of the Vitim River. The research was carried out according to the original methodology developed in the Siberian branches of the GS RAS, using the “Baikal” equipment recording seismic signals continuously (in contrast to traditional cable bottom or ground-based systems with time-limited recording). In this case, patented air guns “Malysh” were used in the water for the excitation of elastic waves, which have been recorded by ground registration method on the riverbank. To this day, the archives store primary materials from each receiving point (about 7000, with a distance of 25 m between them) in the form of a continuous hours-long digital recording of seismic signals and noise. These materials make it possible to generate seismograms of significant duration – up to 23 s, equal to the time interval between wave generations, in contrast to 6–10 second seismograms traditionally used to construct sections of the upper part of the Earth’s crust. In addition, the 24-bit Baikal recorder makes it possible to record signals with an amplitude two orders of magnitude lower than the amplitude of seismic noise. On the seismic sections constructed by the processor at arrival times of up to 13–14 sec, due to an increase in the stack fold of signals up to 1000–2000, we can select low-amplitude waves, reflected from boundaries in the middle and lower parts of the Earth’s crust to the Moho boundary. High fold is achieved by increasing the size of the stacking site (bin) by several times. By processing seismograms, generated from archival materials, for the first time, we have a complete vertical section of the Earth’s crust along a 170 km long profile, using low-amplitude signals. The profile passes through the junction zone of the Angara–Lena monocline and the Bodaibo-Patom folded system. The proposed approach can be used to obtain preliminary (and relatively cheap) information about the deep structure of the Earth’s crust along profiles performed using the CDP-2D river seismic exploration method.

Machine learning for early detection of plant viruses: Analyzing post-infection electrical signal patterns

Plant viral diseases significantly threaten global agricultural productivity, necessitating rapid and non-invasive early detection methods to mitigate losses and prevent disease spread. This study presents a novel approach for early detection of viral infections in plants by analyzing electrical signal patterns, focusing on Alfalfa Mosaic Virus (AMV) infection in tobacco plants. A custom-designed, portable signal recording system was developed, incorporating multiple filtering stages at different steps to minimize noise in field conditions without the need for a Faraday cage, ensuring high-quality signal acquisition. Electrical signals were recorded from both healthy and AMV-infected plants before visible symptoms appeared. By extracting a comprehensive range of features in the low-frequency range of 0–4 Hz and carefully examining specific changes in the electrical signals post-infection, significant alterations were observed, characterized by weakened signal strength and increased irregularity. Through precise feature selection techniques, and using only three key features—Median, Autoregressive coefficients, and Autocorrelation—machine learning models, including Support Vector Machine, K-Nearest Neighbors, and Random Forest, achieved approximately 97% accuracy in detecting virus-infected plants even before the appearance of visual symptoms. This represents superior performance with minimal input data compared to existing methods. These results demonstrate the potential of electrical signal analysis combined with machine learning as a practical, rapid, non-invasive, and affordable tool for early virus detection in plants that is easy to use by non-specialists. Implementing this approach could significantly improve disease management strategies and contribute to sustainable agricultural practices by enabling timely interventions in real-world agricultural environments.

The Extraction Of Neural Strategies From The Surface Emg: 2004-2024.

This review follows two previous papers (Farina et al., 2004, 2014) in which we reflected on the use of surface EMG in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modelling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of non-measurable parameters by inverse modelling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the non-stationarities in dynamic contractions, and to decompose signals in real-time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 years since our first review, we conclude that the recording and analysis of surface EMG signals has seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.

A Nepenthes‐Inspired Hydrogel Hybrid System for Sweat‐Wicking Electrophysiological Signal Recording during Exercises

A <i>Nepenthes</i>‐Inspired Hydrogel Hybrid System for Sweat‐Wicking Electrophysiological Signal Recording during Exercises

Large-Scale, High-Density MicroLED Array-Based Optogenetic Device for Neural Stimulation and Recording.

Optogenetics has emerged as a pivotal tool in neuroscience, enabling precise control of neural activity through light stimulation. However, the current microLED arrays lack sufficient density and scalability. This study proposes an innovative optogenetic device capable of integrating hundreds of microLEDs and electrocorticography (ECOG) electrodes. Individual or multiple microLEDs in the device can be selectively controlled with a custom controller. The light intensity of microLEDs decreases with increasing brain tissue penetration while maintaining a low temperature rise during pulse stimulations. In addition, interference from microLED pulses on ECOG electrode recordings could be alleviated with local mean subtraction data processing. The optogenetic device enables high-quality neural signal recording and triggers a significant enhancement in neural activity following light stimulation. Integration of microLED arrays and ECOG electrodes in the optogenetic device represents a promising advancement in neuroscientific research, providing improved spatial and temporal recording and control over neural activity.

A nerve-adhesive stretchable electrode for stable neural signal recording and stimulation

A nerve-adhesive stretchable electrode for stable neural signal recording and stimulation

Contribution of physiological dynamics in predicting major depressive disorder severity

AbstractThis study aimed to explore the physiological dynamics of cognitive stress in patients with Major Depressive Disorder (MDD) and design a multiparametric model for objectively measuring severity of depression. Physiological signal recordings from 40 MDD patients and 40 healthy controls were collected in a baseline stage, in a stress‐inducing stage using two cognitive tests, and in the recovery period. Several features were extracted from electrocardiography, photoplethysmography, electrodermal activity, respiration, and temperature. Differences between values of these features under different conditions were used as indexes of autonomic reactivity and recovery. Finally, a linear model was designed to assess MDD severity, using the Beck Depression Inventory scores as the outcome variable. The performance of this model was assessed using the MDD condition as the response variable. General physiological hyporeactivity and poor recovery from stress predict depression severity across all physiological signals except for respiration. The model to predict depression severity included gender, body mass index, cognitive scores, and mean heart rate recovery, and achieved an accuracy of 78%, a sensitivity of 97% and a specificity of 59%. There is an observed correlation between the behavior of the autonomic nervous system, assessed through physiological signals analysis, and depression severity. Our findings demonstrated that decreased autonomic reactivity and recovery are linked with an increased level of depression. Quantifying the stress response together with a cognitive evaluation and personalization variables may facilitate a more precise diagnosis and monitoring of depression, enabling the tailoring of therapeutic interventions to individual patient needs.

Wavelet transform-based mode decomposition for EEG signals under general anesthesia.

Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Control methods for intelligent prosthetics based on human-machine interaction

Abstract. Intelligent prosthetics play a crucial role in restoring social functions for people with disabilities. This paper systematically reviews the decoding techniques for motor control information used in intelligent control of prosthetics and the encoding techniques for sensory feedback. The acquisition and decoding of motor information are discussed in terms of signal recording and decoding from the human body and the prosthetic. Sensory feedback in prosthetics is discussed from the perspectives of biomimetic and non-biomimetic feedback. Finally, the development of intelligent prosthetic control is explored, summarizing and forecasting future directions in this field.

Chronic implantable flexible serpentine probe reveals impaired spatial coding of place cells in epilepsy

Abstract Developing minimally invasive and reliable electrode probes for neural signal recording is crucial for advancing neuroscience and treating major brain disorders. Flexible neural probes offer superior long-term recording capabilities over traditional rigid probes. This study introduces a parylene-based serpentine electrode probe for stable, long-term neural monitoring. Inspired by the flexibility and morphology of snakes, the probe's serpentine design ensures stable anchorage within the brain tissue during subject movement. The probe features a hydrophilic surface and is combined with a biodegradable silk fibroin-PEG coating, significantly enhancing biocompatibility and mitigating inflammatory responses. In vivo experiments demonstrate that these probes maintain stable, high-quality neural recordings for over eight months. The probes are also used to investigate the neural bases of epilepsy-induced cognitive deficits. By analyzing place cell dynamics in mice pre- and post-epileptic events, we identified the correlation between impaired spatial encoding and the observed cognitive deficits in epileptic mice. This study highlights the potential of our flexible probes in neurological research and medical applications.

Open-ended molecular recording of sequential cellular events into DNA.

Genetically encoded DNA recorders noninvasively convert transient biological events into durable mutations in a cell's genome, allowing for the later reconstruction of cellular experiences by DNA sequencing. We present a DNA recorder, peCHYRON, that achieves high-information, durable, and temporally resolved multiplexed recording of multiple cellular signals in mammalian cells. In each step of recording, prime editor, a Cas9-reverse transcriptase fusion protein, inserts a variable triplet DNA sequence alongside a constant propagator sequence that deactivates the previous and activates the next step of insertion. Insertions accumulate sequentially in a unidirectional order, editing can continue indefinitely, and high information is achieved by coexpressing a variety of prime editing guide RNAs (pegRNAs), each harboring unique triplet DNA sequences. We demonstrate that the constitutive expression of pegRNA collections generates insertion patterns for the straightforward reconstruction of cell lineage relationships and that the inducible expression of specific pegRNAs results in the accurate recording of exposures to biological stimuli.

A stealthy neural recorder for the study of behaviour in primates.

By monitoring brain neural signals, neural recorders allow for the study of neurological mechanisms underlying specific behavioural and cognitive states. However, the large brain volumes of non-human primates and their extensive range of uncontrolled movements and inherent wildness make it difficult to carry out covert and long-term recording and analysis of deep-brain neural signals. Here we report the development and performance of a stealthy neural recorder for the study of naturalistic behaviours in non-human primates. The neural recorder includes a fully implantable wireless and battery-free module for the recording of local field potentials and accelerometry data in real time, a flexible 32-electrode neural probe with a resorbable insertion shuttle, and a repeater coil-based wireless-power-transfer system operating at the body scale. We used the device to record neurobehavioural data for over 1 month in a freely moving monkey and leveraged the recorded data to train an artificial intelligence model for the classification of the animals' eating behaviours.

Design of a supersoft, ultra-stretchable, and 3D printable hydrogel electrical bioadhesive interface for electromyography monitoring

Electromyography (EMG) monitoring has been extensively employed for critical applications in medicine, sports science, and rehabilitation. However, the mechanical mismatch between conventional EMG electrodes and the skin can lead to electrode detachment upon significant skin deformation. To address this limitation, we develop a PEDOT:PSS-based hydrogel electrical bioadhesive interface (EBI) that incorporates molecular doping and robust adhesion strategies to achieve excellent mechanical compatibility with biological tissues. This hydrogel EBI is fabricated using direct writing of printable inks followed by in-situ thermal initiation, enabling the creation of customizable patterns with high shape fidelity. The resultant 3D-printed PEDOT:PSS-based hydrogel EBI exhibits supersoft properties (Young's modulus 5-8.5 kPa), ultra-stretchability (1,175% strain), robust adhesion (>133 kPa), and outstanding electrochemical performance (CIC reduction by 0.45% over 100,000 cycles). Additionally, we further develop a PEDOT:PSS-based hydrogel electrode specifically for stable EMG signal recording. This electrode outperforms superior signal-to-noise ratio (SNR) performance compared to commercial electrodes in EMG monitoring.

In Vivo Recording of Intracellular Signals from a Single Neuron

In Vivo Recording of Intracellular Signals from a Single Neuron

In Vivo Recording of Intracellular Signals from a Single Neuron

In Vivo Recording of Intracellular Signals from a Single Neuron

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Research on Marine Environmental Noise Based on Acoustic Bait Domestication Technology

Acoustic domestication type of marine pastures are generally established in shallow waters with water depth not exceeding 200 m. In order to study the effect of acoustic domestication, this paper aims to investigate the environmental noise in the shallow waters of the sea. Unlike the stable deep sea environment, the environmental noise in the shallow sea is extremely complex, which has a direct impact on the effect of acoustic domestication. In this paper, in order to study the marine environmental noise, a hydroacoustic signal processing system is established, which is divided into two parts: the hardware part and the software part; the hardware part is mainly for signal sampling, and the software part is for signal processing. The hardware part of the hydroacoustic signal processing system includes three parts: acoustic-electric conversion, intermediate processing and signal recording. The study of ship noise showed that the fishing boat is about 5m long, with a power of 60hp, and passes about 10m away from the hydrophone, and it is more than 10dB higher than that without a ship, when there is a ship passing, in which the 400Hz is 17.82dB higher. Measurements of ocean ambient noise at different depths show that the noise decreases with depth, but when close to the seabed, the noise decreases with depth due to the reflection effect of the seabed on the sound and the biological noise of the seabed.

A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.