Real-time monitoring of cortical brain activity in response to acute pain using wide-area Ca2+ imaging.

Previous human and rodent studies indicated that nociceptive stimuli activate many brain regions that is involved in the somatosensory and emotional sensation. Although these studies have identified several important brain regions involved in pain perception, it has been a challenge to observe neural activity directly and simultaneously in these multiple brain regions during pain perception. Using a transgenic mouse expressing G-CaMP7 in majority of astrocytes and a subpopulation of excitatory neurons, we recorded the brain activity in the mouse cerebral cortex during acute pain stimulation. Both of hind paw pinch and intraplantar administration of formalin caused strong transient increase of the fluorescence in several cortical regions, including primary somatosensory, motor and retrosplenial cortex. This increase of the fluorescence intensity was attenuated by the pretreatment with morphine. The present study provides important insight into the cortico-cortical network during pain perception.

Grand Challenges at the Interface of Engineering and Medicine.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Investigating the Influence of Morphine and Cocaine on the Mesolimbic Pathway Using a Novel Microimaging Platform.

Dopamine (DA)'s relationship with addiction is complex, and the related pathways in the mesocorticolimbic system are used to deliver DA, regulating both behavioral and perceptual actions. Specifically, the mesolimbic pathway connecting the ventral tegmental area (VTA) and the nucleus accumbens (NAc) is crucial in regulating memory, emotion, motivation, and behavior due to its responsibility to modulate dopamine. To better investigate the relationship between DA and addiction, more advanced mapping methods are necessary to monitor its production and propagation accurately and efficiently. In this study, we incorporate dLight1.2 adeno-associated virus (AAV) into our latest CMOS (complementary metal-oxide semiconductor) imaging platform to investigate the effects of two pharmacological substances, morphine and cocaine, in the NAc using adult mice. By implanting our self-fabricated CMOS imaging device into the deep brain, fluorescence imaging of the NAc using the dLight1.2 AAV allows for the visualization of DA molecules delivered from the VTA in real time. Our results suggest that changes in extracellular DA can be observed with this adapted system, showing potential for new applications and methods for approaching addiction studies. Additionally, we can identify the unique characteristic trend of DA release for both morphine and cocaine, further validating the underlying biochemical mechanisms used to modulate dopaminergic activation.

Multi-Region Microdialysis Imaging Platform Revealed Dorsal Raphe Nucleus Calcium Signaling and Serotonin Dynamics during Nociceptive Pain.

In this research, we combined our ultralight micro-imaging device for calcium imaging with microdialysis to simultaneously visualize neural activity in the dorsal raphe nucleus (DRN) and measure serotonin release in the central nucleus of the amygdala (CeA) and the anterior cingulate cortex (ACC). Using this platform, we observed brain activity following nociception induced by formalin injection in the mouse's hind paw. Our device showed that DRN fluorescence intensity increased after formalin injection, and the increase was highly correlated with the elevation in serotonin release in both the CeA and ACC. The increase in calcium fluorescence intensity occurred during the acute and inflammatory phases, which suggests the biphasic response of nociceptive pain. Furthermore, we found that the increase in fluorescence intensity was positively correlated with mouse licking behavior. Lastly, we compared the laterality of pain stimulation and found that DRN fluorescence activity was higher for contralateral stimulation. Microdialysis showed that CeA serotonin concentration increased only after contralateral stimulation, while ACC serotonin release responded bilaterally. In conclusion, our study not only revealed the inter-regional serotonergic connection among the DRN, the CeA, and the ACC, but also demonstrated that our device is feasible for multi-site implantation in conjunction with a microdialysis system, allowing the simultaneous multi-modal observation of different regions in the brain.

Prenatal nicotine exposure alters gene expression profiles of neurons in the sub-regions of the VTA during early postnatal development.

Brain growth occurs during the first 2 weeks of postnatal development in rats. This developmental period is equivalent to the third trimester of human gestation. Dendritic arborization, axonal growth, and gliogenesis are observed along with a strong maturation of neurotransmission during this critical development period. Furthermore, nicotine exposure during early development causes deficiencies in sensory and cognitive processing in adults. In this study, we further investigated the gene expression of neuron groups and the influence of perinatal nicotine exposure on gene expressions of neurons within the sub-regions of the ventral tegmental area (VTA) in 1 week, 2 week and 3-week-old rat pups. We exposed pregnant rats to nicotine perinatally on gestational day 7 through postnatal day 14. Pups are exposed to nicotine during pregnancy and through breastfeeding to investigate its effect in rat pups during early neuronal development. Real time PCR was used to find the relative expressions of gamma-aminobutyric acid (GABA), dopamine, and glutamate neuron markers within the three sub-regions of the VTA including the parabrachial pigmented nucleus (PBP), parainterfascicular (PIF), and paranigral nucleus (PN). Our results indicated that during early maturation, the dopamine marker tyrosine hydroxylase (TH) showed a consistently increased significance in PN sub-region compared to PIF and PBP. These results suggest that following perinatal nicotine exposure, VTA dopamine neurons, especially within the PN sub-region, are significantly excited starting from birth.

Investigating the Modulation of the VTA Neurons in Nicotine-Exposed Pups during Early Maturation Using Optogenetics.

Advancing the understanding of the relationship between perinatal nicotine addiction and the reward mechanism of the brain is crucial for uncovering and implementing new treatments for addiction control and prevention. The mesolimbic pathway of the brain, also known as the reward pathway, consists of two main areas that regulate dopamine (DA) and addiction-related behaviors. The ventral tegmental area (VTA) releases DA when stimulated, causing the propagation of neuronal firing along the pathway. This ends in the release of DA into the extracellular space of the nucleus accumbens (NAc), which is directly modulated by the uptake of DA. Much research has been conducted on the effects of nicotine addiction, but little research has been conducted concerning nicotine addiction and the mesolimbic pathway regarding maturation due to the small brain size. In this study, we apply our novel microstimulation experimental system to rat pups that have been perinatally exposed to nicotine. By using our self-fabricated photo-stimulation (PS) device, we can stimulate the VTA and collect dialysate, which is then used to estimate DA released into the NAc. The proposed platform has demonstrated the potential to monitor neural pathways as the pups mature.

Targeted Sensitization of Glioblastoma Multiforme Using AAAPT Technology.

Glioblastoma Multiforme (GBM) is the most malignant type of all brain tumors. Current GBM treatment options include surgery, followed by radiation and chemotherapy. However, GBM can become resistant to therapy, resulting in tumor recurrence. GBM cells develop resistance to treatments by either downregulating cell death pathways (CD95) or upregulating cell survival pathways (NF-κB (p65)). Healthy tissues can be affected by the increased therapeutic dose. Therefore, it is important to develop a method that can only target GBM tumor cells, thereby reducing the non-specific uptake which will reduce the side effects. Here we demonstrate an application of novel priori activation of apoptosis pathways of tumor technology (AAAPT), which has been used to demonstrate the effect of targeted tumor sensitizers to make chemotherapy work at lower doses in breast, lung and prostate cancers. Treatment of GBM spheroids with AAAPT in 3D PEGDA microwells, showed an increase in cell death, an upregulation of cell death pathways, and a downregulation of cell survival pathways, in comparison to Temozolomide (TMZ), an oral alkylating agent, which is a commonly used chemotherapy in the treatment of GBM. The dose of AAAPT sensitizers may provide a promising method to increase treatment efficacy and reduce off-target toxicity, as an alternative to existing methods which cause significant off-target damage.

Processes for Designing Innovative Biomedical Hardware to Use in Space and on Earth.

The new era of space exploration is increasing the astronaut's number and diversity in low orbit and beyond. The influx of such a diverse crew population will also increase the need for medical technologies to ensure safe and productive missions. Such a need represents a unique opportunity to innovate and develop diagnostics and treatment tools to meet future needs. Historically, terrestrial regulatory oversight of biomedical design processes was considered separate from spaceflight regulatory processes because it did not address spaceflight constraints. These constraints challenge the creative development of unique solutions for use in space. Translation between healthcare innovation in spaceflight to healthcare on Earth and vice versa requires understanding the commonalities, unique needs and constraints. This manuscript provides a framework for comparing Earth-space design processes and a perspective on the best practices to improve healthcare equity and health outcomes.

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring.

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.

Investigating miRNA-mRNA Interactions and Gene Regulatory Networks From VTA Dopaminergic Neurons Following Perinatal Nicotine and Alcohol Exposure Using Bayesian Network Analysis.

MicroRNAs play an important role in gene regulation for many biological systems, including nicotine and alcohol addiction. However, the underlying mechanism behind miRNAs and mRNA interaction is not well characterized. Microarrays are commonly used to quantify the expression levels of mRNAs and/or miRNAs simultaneously. In this study, we performed a Bayesian network analysis to identify mRNA and miRNA interactions following perinatal exposure to nicotine and/or alcohol. We utilized three sets of microarray data to predict the regulation relationship between mRNA and miRNAs. Following perinatal alcohol exposure, we identified two miRNAs: miR-542-5p and miR-874-3p, that exhibited a strong mutual influence on several mRNA in gene regulatory pathways, mainly Axon guidance and Dopaminergic synapses. Finally, we confirmed our predicted addiction pathways based on the Bayesian network analysis with the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)-based database and identified comparable relevant miRNA-mRNA pairs. We believe the Bayesian network can provide insight into the complexity biological process related to addiction and can potentially be applied to other diseases.

New Horizons: From Vision to Impact.

Over the past two years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated coronavirus disease (COVID-19), have greatly impacted our lives. This pandemic has revealed how our health care systems were not prepared for this major challenge, especially with respect to treatment, rapid diagnosis, and tracking, as well as limited hospital equipment, staff members, and resources. COVID-19 will be one of the deadliest pandemics in modern history and continues to strain resources available not only to health care workers, but also the general public, while the number of infected people and cases continues to drastically increase daily, especially within the United States and Europe.

Investigating the Influence of GABA Neurons on Dopamine Neurons in the Ventral Tegmental Area Using Optogenetic Techniques.

Dopamine (DA) is the key regulator of reward behavior. The DA neurons in the ventral tegmental area (VTA) and their projection areas, which include the prefrontal cortex (PFC), nucleus accumbens (NAc), and amygdala, play a primary role in the process of reward-driven behavior induced by the drugs of addiction, including nicotine and alcohol. In our previous study, we developed a novel platform consisting of micro-LED array devices to stimulate a large area of the brain of rats and monkeys with photo-stimulation and a microdialysis probe to estimate the DA release in the PFC. Our results suggested that the platform was able to detect the increased level of dopamine in the PFC in response to the photo-stimulation of both the PFC and VTA. In this study, we used this platform to photo-stimulate the VTA neurons in both ChrimsonR-expressing (non-specific) wild and dopamine transporter (DAT)-Cre (dopamine specific) mice, and measured the dopamine release in the nucleus accumbens shell (NAcShell). We measured the DA release in the NAcShell in response to optogenetic stimulation of the VTA neurons and investigated the effect of GABAergic neurons on dopaminergic neurons by histochemical studies. Comparing the photo-stimulation frequency of 2 Hz with that of 20 Hz, the change in DA concentration at the NAcShell was greater at 20 Hz in both cases. When ChrimsonR was expressed specifically for DA, the release of DA at the NAcShell increased in response to photo-stimulation of the VTA. In contrast, when ChrimsonR was expressed non-specifically, the amount of DA released was almost unchanged upon photo-stimulation. However, for nonspecifically expressed ChrimsonR, intraperitoneal injection of bicuculline, a competitive antagonist at the GABA-binding site of the GABAA receptor, also significantly increased the release of DA at the NAcShell in response to photo-stimulation of the VTA. The results of immunochemical staining confirm that GABAergic neurons in the VTA suppress DA activation, and also indicate that alterations in GABAergic neurons may have serious downstream effects on DA activity, NAcShell release, and neural adaptation of the VTA. This study also confirms that optogenetics technology is crucial to study the relationship between the mesolimbic dopaminergic and GABAergic neurons in a neural-specific manner.

Predicting Feeding Conditions of Premature Infants Through Non-Nutritive Sucking Skills Using a Sensitized Pacifier.

Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we present a new device that can measure both intraoral pressure (IP) and expression pressure (EP) as ororhithmic behavior parameters of non-nutritive sucking skills in infants. Our device is low-cost, easy-to-use, and accurate, which makes it appropriate for extensive studies. To showcase one of the applications of our device, we collected weekly data from 137 premature infants from 29 week-old to 36 week-old. Around half of the infants in our study needed intensive care even after they were 36 week-old. We call them full attainment of oral feeding (FAOF) infants. We then used the Non-nutritive sucking (NNS) features of EP and IP signals of infants recorded by our device to predict FAOF infants' sucking conditions. We found that our pipeline can predict FAOF infants several weeks before discharge from the hospital. Thus, this application of our device presents a robust and inexpensive alternative to monitor oral feeding ability in premature infants.

Healthcare Innovations to Address the Challenges of the COVID-19 Pandemic.

We have been faced with an unprecedented challenge in combating the COVID-19/SARS-CoV2 outbreak that is threatening the fabric of our civilization, causing catastrophic human losses and a tremendous economic burden globally. During this difficult time, there has been an urgent need for biomedical engineers, clinicians, and healthcare industry leaders to work together to develop novel diagnostics and treatments to fight the pandemic including the development of portable, rapidly deployable, and affordable diagnostic testing kits, personal protective equipment, mechanical ventilators, vaccines, and data analysis and modeling tools. In this position paper, we address the urgent need to bring these inventions into clinical practices. This paper highlights and summarizes the discussions and new technologies in COVID-19 healthcare, screening, tracing, and treatment-related presentations made at the IEEE EMBS Public Forum on COVID-19. The paper also provides recent studies, statistics and data and new perspectives on ongoing and future challenges pertaining to the COVID-19 pandemic.

Cancelable HD-SEMG Biometric Identification via Deep Feature Learning.

Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings.

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Investigating the effects of chronic perinatal alcohol and combined nicotine and alcohol exposure on dopaminergic and non-dopaminergic neurons in the VTA.

The ventral tegmental area (VTA) is the origin of dopaminergic neurons and the dopamine (DA) reward pathway. This pathway has been widely studied in addiction and drug reinforcement studies and is believed to be the central processing component of the reward circuit. In this study, we used a well-established rat model to expose mother dams to alcohol, nicotine-alcohol, and saline perinatally. DA and non-DA neurons collected from the VTA of the rat pups were used to study expression profiles of miRNAs and mRNAs. miRNA pathway interactions, putative miRNA-mRNA target pairs, and downstream modulated biological pathways were analyzed. In the DA neurons, 4607 genes were differentially upregulated and 4682 were differentially downregulated following nicotine-alcohol exposure. However, in the non-DA neurons, only 543 genes were differentially upregulated and 506 were differentially downregulated. Cell proliferation, differentiation, and survival pathways were enriched after the treatments. Specifically, in the PI3K/AKT signaling pathway, there were 41 miRNAs and 136 mRNAs differentially expressed in the DA neurons while only 16 miRNAs and 20 mRNAs were differentially expressed in the non-DA neurons after the nicotine-alcohol exposure. These results depicted that chronic nicotine and alcohol exposures during pregnancy differentially affect both miRNA and gene expression profiles more in DA than the non-DA neurons in the VTA. Understanding how the expression signatures representing specific neuronal subpopulations become enriched in the VTA after addictive substance administration helps us to identify how neuronal functions may be altered in the brain.

Recommendation to Use Wearable-Based mHealth in Closed-Loop Management of Acute Cardiovascular Disease Patients During the COVID-19 Pandemic.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model.

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called "MobileNetV2," which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification.

With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.