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Ramírez-Pérez V, Guerrero-Díaz-de-León JA, Macías-Díaz JE. On the detection of activity patterns in electromyographic signals via decision trees. EVOLUTIONARY INTELLIGENCE 2023. [DOI: 10.1007/s12065-023-00844-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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2
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Nacpil EJC, Nacy S, Youssef G. Feasibility assessment of transfer functions describing biomechanics of the human lower limb during the gait cycle. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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3
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Zheng Y, Hu X. Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information. Int J Neural Syst 2021; 31:2150010. [PMID: 33541251 DOI: 10.1142/s0129065721500106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.
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Affiliation(s)
- Yang Zheng
- Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill and North Carolina State University, Raleigh, NC, USA
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill and North Carolina State University, Raleigh, NC, USA
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Li Y, Chen J, Yang Y. A Method for Suppressing Electrical Stimulation Artifacts from Electromyography. Int J Neural Syst 2019; 29:1850054. [DOI: 10.1142/s0129065718500545] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.
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Affiliation(s)
- Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
| | - Jun Chen
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
| | - Yuan Yang
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Abstract
Orthotic devices are defined as externally applied devices that are used to modify the structural and functional characteristics of the neuro-muscular and skeletal systems. The aim of the current study is to improve the control and movement of a robotic arm orthosis by means of an intelligent optimization system. Firstly, the control problem settlement is defined with the muscle, brain, and arm model. Subsequently, the optimization control, which based on a differential evolution algorithm, is developed to calculate the optimum gain values. Additionally, a cost function is defined in order to control and minimize the effort that is made by the subject and to assure that the algorithm follows as close as possible the defined setpoint value. The results show that, with the optimization algorithm, the necessary development force of the muscles is close to zero and the neural excitation level of biceps and triceps signal values are getting lower with a gain increase. Furthermore, the necessary development force of the biceps muscle to overcome a load added to the orthosis control system is practically the half of the one that is necessary without the optimization algorithm.
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Cai S, Chen Y, Huang S, Wu Y, Zheng H, Li X, Xie L. SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training. Front Neurorobot 2019; 13:31. [PMID: 31214010 PMCID: PMC6558101 DOI: 10.3389/fnbot.2019.00031] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 05/09/2019] [Indexed: 11/28/2022] Open
Abstract
Robot-assisted rehabilitation is a growing field that can provide an intensity, quality, and quantity of treatment that exceed therapist-mediated rehabilitation. Several control algorithms have been implemented in rehabilitation robots to develop a patient-cooperative strategy with the capacity to understand the intention of the user and provide suitable rehabilitation training. In this paper, we present an upper-limb motion pattern recognition method using surface electromyography (sEMG) signals with a support vector machine (SVM) to control a rehabilitation robot, ReRobot, which was built to conduct upper-limb rehabilitation training for post-stroke patients. For poststroke rehabilitation training using the ReRobot, the upper-limb motion of the patient's healthy side is first recognized by detecting and processing the sEMG signals; then, the ReRobot assists the impaired arm in conducting mirror rehabilitation therapy. To train and test the SVM model, five healthy subjects participated in the experiments and performed five standard upper-limb motions, including shoulder flexion, abduction, internal rotation, external rotation, and elbow joint flexion. Good accuracy was demonstrated in experimental results from the five healthy subjects. By recognizing the model motion of the healthy side, the rehabilitation robot can provide mirror therapy to the affected side. This method can be used as a control strategy of upper-limb rehabilitation robots for self-rehabilitation training with stroke patients.
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Affiliation(s)
- Siqi Cai
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Yan Chen
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Shuangyuan Huang
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Yan Wu
- ASTAR Institute for Infocomm Research, Singapore, Singapore
| | - Haiqing Zheng
- The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xin Li
- The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
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Lu Z, Chen X, Zhang X, Tong KY, Zhou P. Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition. Int J Neural Syst 2017; 27:1750009. [DOI: 10.1142/s0129065717500095] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.
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Affiliation(s)
- Zhiyuan Lu
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA
- TIRR Memorial Hermann Research Center, 1333B Moursund St., Houston, TX, USA
| | - Xiang Chen
- Biomedical Engineering Program, University of Science and Technology of China, Hefei, P. R. China
| | - Xu Zhang
- Biomedical Engineering Program, University of Science and Technology of China, Hefei, P. R. China
| | - Kay-Yu Tong
- Division of Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, P. R. China
| | - Ping Zhou
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA
- TIRR Memorial Hermann Research Center, 1333B Moursund St., Houston, TX, USA
- Guangdong Work Injury Rehabilitation Center, 68 Qide Rd., Guangzhou, P. R. China
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Karimimehr S, Marateb HR, Muceli S, Mansourian M, Mañanas MA, Farina D. A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings. Int J Neural Syst 2017; 27:1750025. [PMID: 28427290 DOI: 10.1142/s0129065717500253] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was [Formula: see text], [Formula: see text], and [Formula: see text], respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were [Formula: see text], and [Formula: see text], respectively. The time cost for processing each 200[Formula: see text]ms iEMG interval was [Formula: see text] (21-97)[Formula: see text]ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was [Formula: see text]% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.
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Affiliation(s)
- Saeed Karimimehr
- Faculty of Engineering, Biomedical Engineering Department, University of Isfahan, HezarJerib st., 81746-73441 Isfahan, Iran
- Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5746 Tehran, Iran
| | - Hamid Reza Marateb
- Faculty of Engineering, Biomedical Engineering Department, University of Isfahan, HezarJerib st., 81746-73441 Isfahan, Iran
- Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya BarcelonaTech (UPC), 08028 Barcelona, Spain
| | - Silvia Muceli
- Institute of Neurorehabilitation Systems, University Medical Center Göttingen, Georg-August University, 37075 Göttingen, Germany
- Clinic for Trauma Surgery, Orthopaedics and Plastic Surgery, University Medical Center Göttingen, Göttingen, Germany
| | - Marjan Mansourian
- Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, HezarJerib St., 81745 Isfahan, Iran
| | - Miguel Angel Mañanas
- Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya BarcelonaTech (UPC), 08028 Barcelona, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 08028, Barcelona, Spain
| | - Dario Farina
- Department of Bioengineering, Imperial College London, SW7 2AZ London, UK
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Ortiz-Rosario A, Adeli H, Buford JA. MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates. Behav Brain Res 2016; 317:226-236. [PMID: 27650101 DOI: 10.1016/j.bbr.2016.09.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 09/07/2016] [Accepted: 09/11/2016] [Indexed: 01/07/2023]
Abstract
Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.
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Affiliation(s)
| | - Hojjat Adeli
- Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States.
| | - John A Buford
- Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, 453 W 10th Ave, Rm. 516E, Columbus, OH 43210, United States
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