Search Results (269)

Background: Dopamine (DA) is a critical neurotransmitter that regulates many physiological and behavioral processes. The central dopaminergic system plays a pivotal role in modulating general anesthesia (GA). DA release in the brain is mainly concentrated in the nucleus accumbens (NAc), prefrontal cortex, hypothalamus, and dorsal striatum. Several NAc neuron subtypes are essential for modulating states of consciousness during GA. However, whether NAc DA signal dynamics correlate with different states of consciousness under sevoflurane anesthesia remains to be elucidated. In this study, we measured the dynamic fluctuations of NAc DA levels throughout sevoflurane anesthesia to verify its role. Methods: An intensity-based genetically encoded DA indicator, dLight1.1, was employed to track DA release in the NAc. Fiber photometry combined with electroencephalogram/electromyogram recordings was employed to synchronously track NAc DA signal dynamics across different states of consciousness under sevoflurane anesthesia. Results: Under 2.5% sevoflurane exposure, DA release in the NAc significantly increased during the initial 100 s of sevoflurane induction, which was designated as sevo on-1 (mean ± standard error of the mean [SEM]; baseline vs. sevo on-1, p = 0.0261), and continued to decrease in the subsequent anesthesia maintenance phases (sevo on-1 vs. sevo on-4, p = 0.0070). Following the cessation of sevoflurane administration (with intervals denoted as sevooff), NAc DA gradually returned to baseline levels (sevo on-1 vs. sevo off-1, p = 0.0096; sevo on-1 vs. sevo off-3, p = 0.0490; sevo on-1 vs. sevo off-4, p = 0.0059; sevo on-4 vs. sevo off-4, p = 0.0340; sevo off-1 vs. sevo off-4, p = 0.0451). During the induction phase, NAc DA signal dynamics markedly increased during the pre-loss of consciousness (LOC) period (pre-anesthesia baseline vs. pre-LOC, p = 0.0329) and significantly declined after LOC (pre-LOC vs. post-LOC, p = 0.0094). For the emergence period, NAc DA release exhibited a noticeable increase during the initial period after recovery of consciousness (ROC) (anesthesia baseline vs. post-ROC, p = 0.0103; pre-ROC vs. post-ROC, p = 0.0086). Furthermore, the DA signals peaked rapidly upon the initiation of the burst wave and then gradually attenuated, indicating a positive correlation with the burst wave onset during burst suppression events. Conclusions: Our findings revealed that NAc DA neurotransmitter signal dynamics correlate with different states of consciousness throughout sevoflurane anesthesia. Full article

Knee joint stiffness occurs and severely limits its range of motion (ROM) after facture around the knee. During mobility training, knee joints need to be flexed to the maximum angle position (maxAP) that can induce pain at an appropriate level in order to pull apart intra-articular adhesive structures while avoiding secondary injuries. However, the maxAP varies with training and is mostly determined by the pain level of patients. In this study, the feasibility of utilizing electromyogram (EMG) activities to detect maxAP was investigated. Specifically, the maxAP detection was converted into a binary classification between pain level three of the numerical rating scales (pain) and below (painless) according to clinical requirements. Firstly, 12 post-fracture patients with knee joint stiffness participated in Experiment I, with a therapist performing routine mobility training and EMG signals being recorded from knee flexors and extensors. The results showed that the extracted EMG features were significantly different between the pain and painless states. Then, the maxAP estimation performance was tested on a knee rehabilitation robot in Experiment II, with another seven patients being involved. The support vector machine and random forest models were used to classify between pain and painless states and obtained a mean accuracy of 87.90% ± 4.55% and 89.10% ± 4.39%, respectively, leading to an average estimation bias of 6.5° ± 5.1° and 4.5° ± 3.5°. These results indicated that the pain-induced EMG can be used to accurately classify pain states for the maxAP estimation in post-fracture mobility training, which can potentially facilitate the application of robotic techniques in fracture rehabilitation. Full article

Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality. This paper explores the potential of utilizing electromyogram (EMG) and electroencephalogram (EEG) signals to not only decipher movement across multiple degrees of freedom (DOFs) but also offer a more intuitive means of control. In this study, six distinct control schemes for upper-limb prosthetic devices are proposed, each with different combinations of EEG and EMG signals. These schemes were designed to control multiple degrees-of-freedom movements, encompassing five different hand and forearm actions (hand-open, hand-close, wrist pronation, wrist supination, and rest-state). Using Linear Discriminant Analysis as a model results in classification accuracies of over 85% for combined EEG-EMG control schemes. The results suggest promising advancements in the field and show the potential for a more effective and user-friendly control interface for upper-limb prosthetic devices. Full article

In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their practical applications. In contrast, conductive hydrogels (CHs) for electronic skin (E-skin) and healthcare monitoring have attracted substantial interest owing to outstanding features, including adjustable mechanical properties, intrinsic flexibility, stretchability, transparency, and diverse functional and structural designs. Considerable efforts focus on developing CHs incorporating various conductive materials to enable multifunctional wearable sensors and flexible electrodes, such as metals, carbon, ionic liquids (ILs), MXene, etc. This review presents a comprehensive summary of the recent advancements in CHs, focusing on their classifications and practical applications. Firstly, CHs are categorized into five groups based on the nature of the conductive materials employed. These categories include polymer-based, carbon-based, metal-based, MXene-based, and ionic CHs. Secondly, the promising applications of CHs for electrophysiological signals and healthcare monitoring are discussed in detail, including electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory monitoring, and motion monitoring. Finally, this review concludes with a comprehensive summary of current research progress and prospects regarding CHs in the fields of electronic skin and health monitoring applications. Full article

Background: Reaction time is analyzed in various situations in sporting events and is reported to be so important that it can make the difference between victory and defeat. This study focused on teeth clenching resulting in remote muscle activation, and examined whether it improves performance of reaction time. This study examined the effects of clenching and clenching strength on the systemic simple reaction time. Methods: This study included 20 healthy adults with normal clenching and a right dominant foot. The task movement for the systemic simple reaction time measurement was a 30 cm forward step. The following three clenching conditions were used: no clenching without dental contact (no-bite condition), a condition in which the participants were instructed to clench with moderate strength (moderate condition), and a condition in which the participants clenching with maximum effort (max condition). The analysis items were release time, grounding time, soleus muscle (Sol) reaction time, and masseter muscle activity. Results: The max condition significantly reduced the reaction time compared with the no-bite condition. Sol reaction and grounding times showed a negative correlation between clenching strength under moderate conditions and the rate of change in reaction time under no-bite and moderate conditions. Release time exhibited no significant correlation between clenching strength under the moderate condition and the rate of change in reaction time under the no-bite and moderate conditions. The remote facilitation effect of clenching improved the systemic reaction time by producing immediate muscle activity. Conclusions: Clenching shortens the systemic simple reaction time. This finding highlights the potential importance of clenching in enhancing performance during sporting events. Full article

Persons with personality type D are characterized by an “unhealthy lifestyle”, which is manifested by low physical activity, less healthy eating behavior, and failure to comply with doctors’ recommendations. Persons with personality type D have an inadequate response of hemodynamic parameters to psychoemotional stress; the response of other parameters has not been sufficiently studied. The aim of this study was to investigate the association of personality type D with various psychophysiological parameters of the body during mental stress in healthy individuals. Material and Methods: The study involved 79 students of Kemerovo State Medical University aged 18 to 32 years (mean age 20.7 ± 2.4 years). Psychophysiological diagnostics was carried out using the BOSLAB complex; electromyogram, electrocardiogram, body temperature, respiration, galvanic skin response, and photoplethysmogram data were recorded. The stress testing protocol included cognitive tasks and recovery phases. Additionally, the presence of personality type D in students was assessed using the DS-14 questionnaire. The results of stress tests were compared in groups with the presence/absence of type D. Results: The frequency of detection of type D was high (54.4%). When examining the response of psychophysiological parameters, the most pronounced response to stress tests with mental load was noted for heart rate variability and respiratory system parameters. Individuals with type D personality showed more pronounced sympathetic activation in response to mental stress and a slower recovery at rest. Among the studied parameters, association with personality type D was noted for the following indicators during the mental arithmetic test: heart rate (p = 0.022), the Baevsky strain index (p = 0.004), respiratory rate (p = 0.020), and an indicator of regulatory process adequacy (p < 0.001). Conclusion: In the present study, we found differences in the reaction of psychophysiological parameters to mental stress in healthy individuals depending on the presence or absence of personality type D. These data can be useful for developing stress resistance programs and biofeedback training. The possibility of using the above psychophysiological parameters in biofeedback training programs for individuals with personality type D requires further research. Full article

Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients. Full article

This study investigated the effects of lemon verbena extract (LVE) on sleep regulation using both a pentobarbital-induced sleep model and an EEG-based sleep assessment model in mice. To elucidate its potential mechanisms, mice were randomly assigned to five groups: control, positive control (diazepam, 2 mg/kg b.w.), and three LVE-treated groups receiving 40, 80, or 160 mg/kg b.w. via oral administration. In the pentobarbital-induced sleep model, mice underwent a two-week oral administration of LVE, followed by intraperitoneal pentobarbital injections. The results demonstrated that LVE significantly shortened sleep latency and prolonged sleep duration compared to the control group. Notably, adenosine A1 receptor expression, both at the mRNA and protein levels, was markedly upregulated in the brains of LVE-treated mice. Furthermore, LVE’s administration led to a significant increase in the mRNA expression of gamma-aminobutyric acid type A (GABA_A) receptor subunits (α2 and β2) in brain tissue. In the electroencephalography (EEG)/electromyogram (EMG)-based sleep model, mice underwent surgical implantation of EEG and EMG electrodes, followed by one week of LVE administration. Quantitative EEG analysis revealed that LVE treatment reduced wakefulness while significantly enhancing REM and NREM sleep’s duration, indicating its potential sleep-promoting effects. These findings suggest that LVE may serve as a promising natural sleep aid, improving both the quality and duration of sleep through the modulation of adenosine and GABAergic signaling pathways. Full article

Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article

Background: Hereditary immune-mediated polymyositis has been reported in the Kooiker dog breed, associated with a 39 kb deletion and low penetrance. Approximately 10–20 percent of homozygous dogs and 0.5–2 percent of heterozygous dogs develop polymyositis. This study examines whether magnetic resonance imaging (MRI) can assist in diagnosing polymyositis in this breed. Methods: All dogs in this prospective case study were purebred Kooiker dogs referred for clinical examination to assess them for polymyositis. A dataset was compiled, including sex, neuter status, and, if applicable, age of onset, clinical signs, CK activity, electromyogram, and histopathological findings. MRI was performed using a 1.5 Tesla MRI scanner, with T1-weighted, T2-weighted, T2W fat-suppressed short tau inversion recovery (STIR), and T1-weighted post-contrast sequences. Results: Five Kooiker dogs were included in the study. Four dogs exhibited clinical signs compatible with polymyositis (one heterozygous and three homozygous for the 39 kb deletion), while one dog was homozygous for the 39 kb deletion but showed no clinical signs. The clinically affected dogs exhibited T2-weighted, STIR, and T1-weighted post-contrast muscular hyperintensity, and the diagnosis was confirmed with histopathology. The asymptomatic dog displayed no MRI abnormalities. Conclusions: MRI has proven to be a valuable tool in assisting with the diagnosis of Kooiker dogs carrying the 39 kb deletion. MRI can act as a screening tool for dogs with the 39 kb deletion, eliminating the need for an initial biopsy. A muscle biopsy, following a confirmatory MRI, is still the preferred method for diagnosing polymyositis. Full article

Muscle activity is composed of fast and slow activations. The detection of the onset time of the electromyogram signal, which is slowly activated, is difficult. This paper proposes a detection method based on marginal spectral entropy (MSE). The surface electromyography (sEMG) signal of the soleus during normal walking was collected by a wireless electromyography acquisition system. The proposed MSE-based detection method is based on the Hilbert–Huang transform (HHT) combined with information entropy. By comparing the changes in MSE before and after muscle activation to plot a trend line, the point of fastest change on the trend line was defined as the onset time of muscle activation. This method was compared with the amplitude threshold method and the Teager–Kaiser energy (TKE) operator method. The results show that the onset time of muscle activation detected by this method is 0.14 s earlier than the amplitude threshold method and 0.16 s earlier than the TKE operator method. The detection results were significantly different (p < 0.05), indicating that this method has higher detection accuracy for the onset time of the sEMG signal, which is slowly activated. Full article

This study aims to apply standard statistics and InterCriteria analysis (ICrA) for assessing the effects of different transcranial magnetic stimulation (TMS) intensities and three muscle conditions on the evoked responses of the first dorsal interosseous muscle (FDIM). Surface electromyograms from the right FDIM of ten right-handed healthy volunteers were recorded, and amplitudes of motor evoked potentials (MEPs), latencies of MEPs, and silent periods were obtained. ICrA was used for the first time as a supplementary tool along with the applied statistical methods. Three case studies were processed by the ICrA approach for a wide examination of neuromuscular excitability in humans. As a result, the relations between increasing TMS intensities, MEP amplitudes, MEP latencies, and silent periods were established at relaxed muscle condition, isometric index finger abduction condition, and co-contraction of antagonist muscles condition. Also, the dependencies between MEP amplitudes, MEP latencies, and silent periods themselves, and for different TMS intensities, were outlined. The results confirmed relations known from the literature and showed new ones. Full article

Conducting research on the effectiveness of driving simulators is of great significance for enhancing the availability of driving simulator systems. However, in risky environments, traditional methods have the limitation of making it difficult to conduct real vehicle experiments. Therefore, this study proposes a method based on driver physiological indicators to evaluate the effectiveness of driving simulators in risky environments. On the one hand, the two-dimensional extended time to collision theoretical model (2D-TTC) was used to calculate the risk degree. Then, the similarity between the risk degree and the drivers’ electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) data sequences was calculated based on the dynamic time warping (DTW) model. On the other hand, we used the complexity and sample entropy of ECG and EMG as indicators to assess the drivers’ physiological load. This paper used intersections as risk scenarios to conduct driving simulation experiments to verify the feasibility of the above method. It was found that changes in drivers’ physiological indicators were consistent with changes in risk degree, with the DTW values of risk degree and drivers’ EDA tending to become smaller and the two sequence values closer to being similar. It was also found that the complexity and the sample entropy of the driver’s ECG and EMG showed higher values in the simulated poor sight intersection scenario compared to the intersection with good sight. In addition, in the simulated heavy traffic intersection scenario, physiological parameters such as EMG complexity and sample entropy, as well as ECG complexity, were higher than in the low traffic flow intersection. These findings are highly consistent with the characteristics of physiological responses in real driving environments, fully demonstrating the effectiveness of the test-driving simulation system in simulating risky traffic scenarios. The method proposed in this paper overcomes the limitations of traditional approaches and effectively validates the effectiveness of driving simulation systems in risky environments. The research results can drive further development and application of driving simulation technology. Full article

With the rapid development of wearable technology, multifunctional sensors have demonstrated immense application potential. However, the limitations of traditional rigid materials restrict the flexibility and widespread adoption of such sensors. Hydrogels, as flexible materials, provide an effective solution to this challenge due to their excellent stretchability, biocompatibility, and adaptability. This study developed a multifunctional flexible sensor based on a composite hydrogel of polyvinyl alcohol (PVA) and sodium alginate (SA), using poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT:PSS) as the conductive material to achieve multifunctional detection of strain, temperature, and physiological signals. The sensor features a simple fabrication process, low cost, and low impedance. Experimental results show that the prepared hydrogel exhibits outstanding mechanical properties and conductivity, with a strength of 118.8 kPa, an elongation of 334%, and a conductivity of 256 mS/m. In strain sensing, the sensor demonstrates a rapid response to minor strains (4%), high sensitivity (gauge factors of 0.39 for 0–120% and 0.73 for 120–200% strain ranges), short response time (2.2 s), low hysteresis, and excellent cyclic stability (over 500 cycles). For temperature sensing, the sensor achieves high sensitivities of −27.43 Ω/K (resistance mode) and 0.729 mV/K (voltage mode), along with stable performance across varying temperature ranges. Furthermore, the sensor has been successfully applied to monitor human motion (e.g., finger bending, wrist movement) and physiological signals such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG), highlighting its significant potential in wearable health monitoring. By employing a simple and efficient fabrication method, this study presents a high-performance multifunctional flexible sensor, offering novel insights and technical support for the advancement of wearable devices. Full article

Brain–computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities. Full article