Chronic Multi-Electrode Electromyography in Snakes.
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ABSTRACT:
SUBMITTER: Jensen GW
PROVIDER: S-EPMC8777293 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Surface electromyography (sEMG) monitoring has recently inspired new applications in the field of patient diagnose, rehabilitation therapy, man-machine-interface and prosthesis control. However, conventional wet electrodes for sEMG recording cannot fully satisfy the requirements of these applications because they are based on rigid metals and conductive gels that cause signal quality attenuation, motion artifact and skin allergy. In this study, a novel flexible dry electrode is presented for sEMG monitoring. The electrode is fabricated by screen-printing a silver-eutectic gallium-indium system over a transfer tattoo paper, which is then hydrographically printed on 3D surface or human skin. Peano curve in open-network pattern is adopted to enhance the mechanics of the electrode. Hydrographic printing enables the electrode to attach to skin intimately and conformably, meanwhile assures better mechanical and electrical properties and therefore improves the signal quality and long-term wearability of the electrode. By recording sEMG signal of biceps under three kinds of movement with comparison to conventional wet electrode, the feasibility of the presented flexible dry electrode for sEMG monitoring was proved.
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Project description:Surface electromyography (sEMG) can provide multiplexed information about muscle performance. If current sEMG electrodes are stretchable, arrayed, and able to be used multiple times, they would offer adequate high-quality data for continuous monitoring. The lack of these properties delays the widespread use of sEMG in clinics and in everyday life. Here, we address these constraints by design of an adhesive dry electrode using tannic acid, polyvinyl alcohol, and PEDOT:PSS (TPP). The TPP electrode offers superior stretchability (~200%) and adhesiveness (0.58 N/cm) compared to current electrodes, ensuring stable and long-term contact with the skin for recording (>20 dB; >5 days). In addition, we developed a metal-polymer electrode array patch (MEAP) comprising liquid metal (LM) circuits and TPP electrodes. The MEAP demonstrated better conformability than commercial arrays, resulting in higher signal-to-noise ratio and more stable recordings during muscle movements. Manufactured using scalable screen-printing, these MEAPs feature a completely stretchable material and array architecture, enabling real-time monitoring of muscle stress, fatigue, and tendon displacement. Their potential to reduce muscle and tendon injuries and enhance performance in daily exercise and professional sports holds great promise.
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Project description:Objective.Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset.Approach.We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time.Main results.Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization.Significance.Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.
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Project description:Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.
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Project description:This paper presents an electromyography (EMG) signal dataset for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking current datasets in the literature or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.
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Project description:Multielectrode arrays (MEAs) allow for acquisition of multisite electrophysiological activity with submillisecond temporal resolution from neural preparations. The signal to noise ratio from such arrays has recently been improved by substrate perforations that allow negative pressure to be applied to the tissue; however, such arrays are not optically transparent, limiting their potential to be combined with optical-based technologies. We present here multi-suction electrode arrays (MSEAs) in quartz that yield a substantial increase in the detected number of units and in signal to noise ratio from mouse cortico-hippocampal slices and mouse retina explants. This enables the visualization of stronger cross correlations between the firing rates of the various sources. Additionally, the MSEA's transparency allows us to record voltage sensitive dye activity from a leech ganglion with single neuron resolution using widefield microscopy simultaneously with the electrode array recordings. The combination of enhanced electrical signals and compatibility with optical-based technologies should make the MSEA a valuable tool for investigating neuronal circuits.
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Project description:The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function of torque measurement is still an extensively used assembly tool. Therefore, wrench torque monitoring is one of the urgent problems that needs to be solved. This study proposes a multi-segmentation parallel convolution neural network (MSP-CNN) model for estimating assembly torque using surface electromyography (sEMG) signals, which is a method of torque monitoring through classification methods. The MSP-CNN model contains two independent CNN models with different or offset torque granularities, and their outputs are fused to obtain a finer classification granularity, thus improving the accuracy of torque estimation. First, a bolt tightening test bench is established to collect sEMG signals and tightening torque signals generated when the operator tightens various bolts using a wrench. Second, the sEMG and torque signals are preprocessed to generate the sEMG signal graphs. The range of the torque transducer is divided into several equal subdivision ranges according to different or offset granularities, and each subdivision range is used as a torque label for each torque signal. Then, the training set, verification set, and test set are established for torque monitoring to train the MSP-CNN model. The effects of different signal preprocessing methods, torque subdivision granularities, and pooling methods on the recognition accuracy and torque monitoring accuracy of a single CNN network are compared experimentally. The results show that compared to maximum pooling, average pooling can improve the accuracy of CNN torque classification and recognition. Moreover, the MSP-CNN model can improve the accuracy of torque monitoring as well as solve the problems of non-convergence and slow convergence of independent CNN network models.
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Project description:ImportanceEffective treatment of the affected hand after stroke is crucial for improved functional independence and recovery.ObjectiveTo determine the feasibility and clinical utility of an electromyography-triggered hand robot.DesignSingle-group repeated-measures design. Participants completed training 3×/wk for 6 wk. Feasibility data included participant feedback, adverse events, and compliance rates. Upper extremity outcomes were collected at baseline, discharge, and 6-wk follow-up.SettingOutpatient clinic.ParticipantsTwelve stroke survivors at least 6 mo poststroke living in the community.InterventionEighteen sessions of intensive robotic hand therapy over 6 wk. Each 60-min treatment session was personalized to match the participant's ability.Outcomes and measuresArm Motor Ability Test (AMAT), Stroke Impact Scale Hand subscale (SIS-H), Stroke Upper Limb Capacity Scale (SULCS), Fugl-Meyer Assessment, Box and Block Test, and dynamometer.ResultsAll participants completed the training phase. Mild skin pinching or rubbing at dorsal proximal interphalangeal joint and proximal arm fatigue were the most common adverse events. Improvements in raw scores were achieved from baseline to discharge for all outcome measures, except the SULCS. Participants significantly improved from baseline to discharge on the AMAT and the SIS-H, and improvements were maintained at 6-wk follow-up.Conclusion and relevanceRobotic hand training was feasible, safe, and well tolerated. Participants reported and demonstrated improvements in functional use of the affected arm. Thirty percent of participants achieved clinically significant improvements on the AMAT. We recommend further study of the device in a larger study using the AMAT as a primary outcome measure.What this article addsIt is feasible and safe to implement a robotic hand training protocol for people with moderate to severe arm impairment in an outpatient setting. Robotic training may provide a viable option for this group to actively participate in intensive training of the distal hand.
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