Muscle Synergies: Use and Validation in Clinics, Robotics, and Sports

Since understanding how the human brain generates neural commands to control muscles during motor tasks still remains an untapped question, great interest is shown in the validation and application of muscle synergies among research groups focused on the electromyography (EMG). In the last decades, the factorization of the EMG signals by means of muscle synergies has been proposed to understand the neurophysiological mechanisms related to the central nervous system ability in reducing the dimensionality of muscle control. For this reason, we planned a special issue on validation and application of the muscle synergy theory to discuss the methodological issues and to propose novel applications in clinics, robotics, and sports. The special issue achieved success among researchers as demonstrated by the large amount of submitted papers and the scientific impact of the published ones. The special issue is composed of twelve manuscripts. Three systematic reviews are included: (i) the first one is focused on the meaning of the muscle synergy theory to understand its applicability as a neurorehabilitation tool (Singh et al.); (ii) the second one is useful to understand the applications of muscle synergies in the investigation of muscle coordination during walking of poststroke patients (Seamon et al.); and (iii) the third one offers a complete overview on the tangible applications of muscle synergies in clinics, robotics, and sports (Taborri et al.). As concerns clinics, the effects of upper limb weakness and task failure, which is the inability to maintain a certain level of force during a task, on the muscle synergies are evaluated by Roh et al. and Castronovo et al., respectively. As regards robotics, the feasibility to use a muscle synergy approach to implement the control system of an upper limb exoskeleton is presented by Chiavenna et al. Moving to the sports, two papers are focused on understanding the muscle synergy organization during the execution of specific technical actions of the badminton (Matsunaga et al. and Barnamehei et al.), one paper shows the muscle synergy structure involved in stability exercises of rhythmic gymnastics (Rutkowska-Kucharska et al.), while the motor control underlying the throwing movement is studied by Cruz-Ruiz et al. Finally, two papers investigate some fundamental methodological issues; in particular concerning the influence of initialization techniques for the application of non-negative matrix factorization (Soomro et al.) and the reliability and repeatability of the methodology for extracting muscle synergies during daily life activities (Taborri et al.). We hope that this special issue can represent an important step to strengthen the use of muscle synergies to explain how the human brain organizes the muscle activation both in clinics and robotics, as well as in sports applications

Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review

In the last years, several studies have been focused on understanding how the central nervous system controls muscles to perform a specific motor task. Although it still remains an open question, muscle synergies have come to be an appealing theory to explain the modular organization of the central nervous system. Even though the neural encoding of muscle synergies remains controversial, a large number of papers demonstrated that muscle synergies are robust across different tested conditions, which are within a day, between days, within a single subject and between subjects that have similar demographic characteristics. Thus, muscle synergy theory has been largely used in several research fields, such as clinics, robotics and sports. The present systematical review aims at providing an overview on the applications of muscle synergy theory in clinics, robotics and sports; in particular, the review is focused on the papers that provide tangible information for: (i) diagnosis or pathology assessment in clinics; (ii) robot-control design in robotics; and (iii) athletes’ performance assessment or training guidelines in sports

EEG recordings as biomarkers of pain perception: where do we stand and where to go?

Introduction: The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception. Methods: A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review. Results: Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception. Conclusion: Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt

Data from: Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications

Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications

Functional Anthropomorphism for Human to Robot Motion Mapping

Abstract-In this paper we propose a generic methodology for human to robot motion mapping for the case of a robotic arm hand system, allowing anthropomorphism. For doing so we discriminate between Functional Anthropomorphism and Perceptional Anthropomorphism, focusing on the first to achieve anthropomorphic solutions of the inverse kinematics for a redundant robot arm. Regarding hand motion mapping, a "wrist" (end-effector) offset to compensate for differences between human and robot hand dimensions is applied and the fingertips mapping methodology is used. Two different mapping scenarios are also examined: mapping for teleoperation and mapping for autonomous operation. The proposed methodology can be applied to a variety of human robot interaction applications, that require a special focus on anthropomorphism

Decoding grasp aperture from motor-cortical population activity

Abstract — The direct neural control of external prosthetic devices such as robot hands requires the accurate decoding of neural activity representing continuous movement. This requirement becomes formidable when multiple degrees of freedom (DoFs) are to be controlled as in the case of the fingers of a robotic hand. In this paper a methodology is proposed for estimating grasp aperture using the spiking activity of multiple neurons recorded with an electrode array implanted in the arm/hand area of primary motor cortex (M1). Grasp aperture provides a reasonable approximation to the hand configuration during grasping tasks, while it offers a large reduction in the number of DoFs that must be estimated. A family of state space models with hidden variables is used to decode each finger grasp aperture with respect to the thumb from a population of motorcortical neurons. The firing rates of multiple neurons in M1 were found to be correlated with grasp aperture and were used as inputs to our decoding algorithm. The proposed decoding architecture was evaluated off-line by decoding pre-recorded neural activity from monkey motor cortex during a natural grasping task. We found that our model was able to accurately reconstruct finger grasp aperture from a small population of cells. This demonstrates the first decoding of continuous grasp aperture from M1 suggesting the feasibility for neural control of prosthetic robotic hands from neuronal population signals. I