Narrow Results

This study investigates an electromyogram (EMG)-based neural interface toward hand rehabilitation for patients with cerebral palsy (CP). Forty-eight channels of surface EMG signals were recorded from the forearm of eight adult subjects with CP, while they tried to perform six different hand grasp patterns. A series of myoelectric pattern recognition analyses were performed to identify the movement intention of each subject with different EMG feature sets and classifiers. Our results indicate that across all subjects high accuracies (average overall classification accuracy > 98%) can be achieved in classification of six different hand movements, suggesting that there is substantial motor control information contained in paretic muscles of the CP subjects. Furthermore, with a feature selection analysis, it was found that a small number of ranked EMG features can maintain high classification accuracies comparable to those obtained using all the EMG features (average overall classification accuracy > 96% with 16 selected EMG features). The findings of the study suggest that myoelectric pattern recognition may be a useful control strategy for promoting hand rehabilitation in CP patients.

Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n : m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay $(43\pm 8\mathrm{\text{ms}})$ between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.

Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.

The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.

In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is $0.8541\pm 0.0398$, and the average mean-squared error (MSE) is $0.6036\pm 0.1226$. The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.

This work presents a neurorobotics model of the brain that integrates the cerebellum and the basal ganglia regions to coordinate movements in a humanoid robot. This cerebellar-basal ganglia circuitry is well known for its relevance to the motor control used by most mammals. Other computational models have been designed for similar applications in the robotics field. However, most of them completely ignore the interplay between neurons from the basal ganglia and cerebellum. Recently, neuroscientists indicated that neurons from both regions communicate not only at the level of the cerebral cortex but also at the subcortical level. In this work, we built an integrated neurorobotics model to assess the capacity of the network to predict and adjust the motion of the hands of a robot in real time. Our model was capable of performing different movements in a humanoid robot by respecting the sensorimotor loop of the robot and the biophysical features of the neuronal circuitry. The experiments were executed in simulation and the real world. We believe that our proposed neurorobotics model can be an important tool for new studies on the brain and a reference toward new robot motor controllers.

Recent related approaches in the areas of vision, motor control and planning are attempting to reduce the computational requirements of each process by restricting the class of problems that can be addressed. Active vision, differential kinematics and reactive planning are all characterized by their minimal use of representations, which simplifies both the required computations and the acquisition of models. This paper describes an approach to visually-guided motor control that is based on active vision and differential kinematics, and is compatible with reactive planning. Active vision depends on an ability to choose a region of the visual environment for task-specific processing. Visual attention provides a mechanism for choosing the region to be processed in a task-specific way. In addition, this attentional mechanism provides the interface between the vision and motor systems by representing visual position information in a 3-D retinocentric coordinate frame. Coordinates in this frame are transformed into eye and arm motor coordinates using kinematic relations expressed differentially. A real-time implementation of these visuomotor mechanisms has been used to develop a number of visually-guided eye and arm movement behaviors.

This study investigated the regularity that characterizes the behavior of dissipative dynamical systems excited by external temporal inputs for pointing movements. Right-handed healthy male participants were asked to continuously point their right index finger at two light-emitting diodes (LEDs) located in the oblique left and right directions in front of them. These movements were performed under two conditions: one in which the direction was repeated and one in which the directions were switched on a stochastic basis. These conditions consisted of 12 tempos (30, 36, 42, 48, 51, 54, 57, 60, 63, 66, 69, and 72 beats per minute). Data from the conditions under which the input pattern was repeated revealed two different trajectories in hyper-cylindrical state space ${ℳ}$, whereas the conditions under which the inputs were switched induced transitions between the two trajectories, which were considered to be excited attractors. The transitions between the two excited attractors were characterized by a self-similar structure. Moreover, the correlation dimensions increased as the tempos increased. These results suggest a relationship of $D\propto 1/T$ ($T$ is the switching-time length; i.e. the condition) between temporal input and pointing behavior and that continuous pointing movements are regular rather than random noise.

The aim of this investigation was to determine how the CNS controlled seven segments of the human deltoid muscle during a change in the direction of shoulder joint motion. Specifically, we wished to determine how the prime mover, synergist and antagonist muscle segments of this muscle were manipulated to assume new functional roles as the direction of shoulder motion was rapidly changed from shoulder abduction to shoulder adduction. Seven bipolar surface electrodes (7 mm inter-electrode distance) were placed over the seven segments (D1–D7) of the right deltoid, in seven young (19–24yrs) male subjects, to detect changes in muscle segment activation as the subjects transitioned from a rapid shoulder-abduction to a rapid-adduction force impulse (MT = 1000 ms). For each subject, fifteen trials were recorded at an inter-trial interval of 30 seconds. Comparisons of muscle segment timing and intensity of activation were made across 6 equal time intervals between just before the peak of the abduction force impulse and the subsequent peak of the adduction force impulse. The results of this study have shown that segments of the deltoid were activated during both the shoulder abduction and shoulder adduction motor task. In addition, the pattern of muscle segment activation (timing and intensity), during the transition from shoulder abduction to shoulder adduction, was dependent upon the muscle's moment arm and line of pull in relation to the axis of shoulder joint rotation. Three distinct patterns of neuromotor activation were noted within the segments of the deltoid muscle. During abduction the agonist prime mover and synergist segments (D1–D5) were totally deactivated (< 10% MVC) as they became antagonist segments during adduction. The antagonist segment (D7), during abduction, was deactivated and then reactivated as it became a synergist segment during adduction. Finally segment D6 was shown to have a nearly continuous period of activation. The study has shown that during a transition to a new movement direction, a muscle segment's line of pull and future function in the next phase of the movement appears to determine its period and intensity of activation.

The aim of this study is to investigate the effect of knee extensors fatigue on joint position sense. Fifteen healthy participants, all males, with no history of previous musculoskeletal lesions were recruited. Evaluation of the knee joint position sense and the muscle fatigue protocol had been performed using an isokinetic dynamometer. Fatigue was considered when the maximum torque was reduced by 50%. The joint position sense was analyzed by the absolute error and the variable error. The paired t-test was used to compare the mean in pre and during muscle fatigue conditions. The level of significance was 5%. Absolute and variable errors were not significantly affected by muscle fatigue. Knee joint position sense does not seem to be affected by fatigue of knee joint extensors.

The elucidation of human locomotion strategies has potential applications in the prevention of sarcopenia and in the reduction of falls. Given the diverse biochemical, mechanical and functional age-related changes seen in the neuro-musculoskeletal system, the decline in motor function is difficult to study experimentally. In this study, we use transfer testing and coupled simulation strategies within a deep reinforcement learning environment to better understand the complex problem of motor control adaptation to age-related changes. Using transfer testing, a 3D musculoskeletal model is separately trained on parameters of the young adult model (Y) for either forward or backward falls after completing two steps forward, and tested using a 30% age-related reduction for all parameters (M_all). This strategy produces a backward fall for a forwardly trained simulation, showing potential sensitivity of these parameters to a given fall direction. Second, a coupled simulation solution is used to simulate recovery from falls by considering the center-of-mass position relative to the base of support. Results for the M_all trained model showed a longer simulation time and a greater vertical pelvis velocity with a maximal value of 4.26m/s. In particular, the results of the coupled simulations clearly show that both the young and M_all condition models respond with a step back and stronger leg extensor activations to propel the model forward to recover from the simulated fall. We developed a novel coupling between transfer testing and coupled simulation strategies to improve upon muscle models for characterizing muscle function, and also to begin testing different hypotheses, such as the strategy and force required to avoid a fall at different limits. This opens new avenues for precision rehabilitation with patient-specific muscle-driven recovery exercises.

When examining postural sway measures of single-leg squat (SLS), there is a lack of consensus on how many trials are required to obtain reliable and clinically relevant data. Forty adults with chronic low back pain performed five consecutive trials of SLS for each side on a portable force plate. The left and right sides were categorized into problem and non-problem sides by Clinical Pilates assessment. SLS performance was characterized by terminal knee flexion angle, squat duration, peak vertical force and postural sway parameters. Data across five trials were first examined with repeated measures analysis of variance; variables with significant differences were further analyzed using intraclass correlation coefficients (ICC). Using all trials as a reference, the reliability of other trial combinations was assessed to examine the potential effects of learning (2-5 squats, 3-5 squats, 4-5 squats), fatigue (1-2 squats, 1-3 squats, 1-4 squats) and steady-state (2-4 squats). For the non-problem side, postural sway measures were highly reliable ($\mathrm{\text{ICC}}\ge 0.9$) regardless of the number of trials analyzed. For the problem side, analyzing the 1-4 squats combination offered consistently reliable results across all postural measures ($\mathrm{\text{ICC}}\ge 0.72$). Thus, it is recommended to analyze the first four consecutive trials to obtain reliable postural sway measures.

The formal theory of the development of early perception and motor control presented here deals with cognitive development as a mapping from a finite set of given experiences to a set of perceptual and motor-control functions. The theory involves seven constraints that uniquely define the mapping. The compatibility with observational phenomena and sufficiency of these constraints shows the validity of the theory. The principle underlying these constraints is a coding by the most efficient representation of information. The efficiency of representation is evaluated by the coding redundancy of given experiences defined as the number of real numbers that characterize experiences plus the size of the minimum continuous decoding function. The coding redundancy of experiences by the most efficient representation corresponds to the Kolmogorov complexity of the experiences. The mapping accounts for the dependence on neonatal experience of the development of perceptual and motor-control functions. This theory of development can also be seen as a metatheory of cognition that presents us a unified view of the diversity of perceptual and motor-control modules.

Two principles of neurocomputational design are implemented into an autonomous real-world device, such as a helicopter. The helicopter has a motivational component towards emitting motor responses in a manner similar to a fledging bird. We expect these two principles, together with an understanding of integrative brain activity and memory in which functionally and selectively distributed neural networks operate in vivo will eventually lead to the embodiment of cognition in a brain-like computer as an engineering counterpart of a real brain.

The rapid growth of cerebellar research is going to clarify several aspects of cellular and circuit physiology. However, the concepts about cerebellar mechanisms of function are still largely related to clinical observations and to models elaborated before the last discoveries appeared. In this paper, the major issues are revisited, suggesting that previous concepts can now be refined and modified. The cerebellum is fundamentally involved in timing and in controlling the ordered and precise execution of motor sequences. The fast reaction of the cerebellum to the inputs is sustained by specific cellular mechanisms ensuring precision on the millisecond scale. These include burst–burst reconversion in the granular layer and instantaneous frequency modulation on the 100-Hz band in Purkinje and deep cerebellar nuclei cells. Precisely timed signals can be used for perceptron operations in Purkinje cells and to establish appropriate correlations with climbing fiber signals inducing learning at parallel fiber synapses. In the granular layer, plasticity turns out to be instrumental to timing, providing a conceptual solution to the discrepancy between cerebellar learning and timing. The granular layer sub-circuit can be tuned by long-term synaptic plasticity and synaptic inhibition to delay the incoming signals over a 100-ms range. For longer sequences, large circuit sections can be entrained into coherent activity in 100-ms cycles. These dynamic aspects, which have not been accounted for by original theories, could in fact represent the essence of cerebellar functioning. It is suggested that the cerebellum can, in this way, operate the realignment of temporally incongruent signals, allowing their binding and pattern recognition in Purkinje cells. The demonstration of these principles, their behavioral relevance and their relationship with internal model theories represent a challenge for future cerebellar research.

Currently, most industrial automation systems and robotic systems require the high-speed transmission of data and highly precise control. We introduce a new communication protocol that is immune to the electromagnetic compatibility (EMC) effect, and is able to reduce the space used in internal robots with limited space. In this paper, we present a novel optical-EtherCAT communication method to reduce EMC, to provide high-speed communication between each module, and to offer real-time control and flexible topology in the internal robot. Then, we verify the communication performance between the proposed optical-EtherCAT communication and previously established EtherCAT communication method. We use the transmission speed, frame size, usage rates of bandwidth, update speed, and cycle time of transmission as evaluation criteria.

Background: Mechanical neck pain (MNP) is one of the most prevalent musculoskeletal pathologies in the present time. Physiotherapy management strategies comprising manual therapy and exercise therapy are routinely administered in patients with MNP.

Objective: To compare the immediate effect of craniocervical flexion (CCF) exercise and Mulligan mobilisation on pain, active cervical range of motion (CROM) and CCF test performance in patients with MNP.

Methods: This prospective, randomised, single-blinded study involved 26 patients with MNP (16 females; mean age; $31.12\pm 8.40$ years) randomised to a single session of active CCF exercise (3 sets of 10 repetitions) or Mulligan mobilisation (3 sets of 6–10 repetitions). Pain intensity was measured on a numerical pain rating scale (NPRS), active CROM was measured using CROM device, and CCF test performance with surface electromyography (EMG) from bilateral sternocleidomastoid (SCM) and anterior scalene (AS) muscles recorded pre- and immediately post-intervention by an assessor blinded to the treatment groups. Mann–Whitney U test was used to analyse between groups and Wilcoxon signed rank test was used to analyse within-group significance for pain and CROM, Cochran–Mantel–Haenszel correlation test was used to analyse the CCF test performance on EMG from the bilateral SCM and AS muscles.

Results: Comparison between pre- and post-intervention readings revealed statistically significant within-group ($p<0.05$) and no between-group significant difference for pain, ROM, and CCF test performance, indicating both interventions were equally effective.

Conclusion: Patients with MNP who received active CCF exercise or Mulligan mobilisation exhibited similar reduction in pain intensity and increased CROM and CCF test performance post-intervention. Surprisingly, AS surface EMG amplitudes were increased post-intervention in both groups warranting further exploration of its role in neck pain.

Background: Nowadays, the development of training programs for speed, agility and reaction time responses in football players is increasing widely. Motor imagery is a new method that uses collateral with physical training. However, there is still a scarcity of evidence concerning the addition of motor imagery protocol to routine training programs.

Objective: The main objective was to compare speed, agility and reaction time after motor imagery training in university athletes and amateur athletes who received and did not receive motor imagery training for 2 weeks.

Methods: Participants were divided into 4 subgroups as follows: university athlete group with motor imagery training and control group, amateur athlete group with motor imagery training and control group. This study collected the training effects of speed, agility and reaction time. The Wilcoxon signed-rank test and the Mann–Whitney U test were selected to analyse the differences within and between groups, respectively.

Results: The result presented positive changes in all variables after training sessions for 2 weeks in all groups. Speed at 20m, agility, and reaction time were found to be significantly different after motor imagery training in both university athletes and amateur athletes.

Conclusion: This finding demonstrated that the addition of the motor imagery training along with routine physical training promotes physical performance in athletes at all experience levels. In further studies, the retention effect after practice should be considered.

Numerous disciplines are engaged in studies involving motor control. In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency.

The system is highly nonlinear in all its physical and physiological attributes. The major physiological characteristics of this system are simultaneous activation of a pair of nonlinear muscle-like-actuators for control purposes, existence of nonlinear spindle-like sensors and Golgi tendon organ-like sensor, actions of gravity and external loading. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex loops. The profile of excitation is difficult to predict a priori, hence we have used a reinforcement learning approach to track a desired trajectory.

This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by State-Action-Reward-State-Action (SARSA) method. The Critic will train the Actor by supervisory learning based on previous experiences. The system is implemented on a PC using Matlab^® and Simulink^® Software. To enhance the computational performance a number of C codes were also written.

The effectiveness and the biological plausibility of the present model are demonstrated by several simulations.

The system showed excellent tracking capability and after 280 epochs the RMS error for position and velocity profiles were 0.02, 0.04 radian and radian/sec, respectively.

Recent related approaches in the areas of vision, motor control and planning are attempting to reduce the computational requirements of each process by restricting the class of problems that can be addressed. Active vision, differential kinematics and reactive planning are all characterized by their minimal use of representations, which simplifies both the required computations and the acquisition of models. This paper describes an approach to visually-guided motor control that is based on active vision and differential kinematics, and is compatible with reactive planning. Active vision depends on an ability to choose a region of the visual environment for task-specific processing. Visual attention provides a mechanism for choosing the region to be processed in a task-specific way. In addition, this attentional mechanism provides the interface between the vision and motor systems by representing visual position information in a 3-D retinocentric coordinate frame. Coordinates in this frame are transformed into eye and arm motor coordinates using kinematic relations expressed differentially. A real-time implementation of these visuomotor mechanisms has been used to develop a number of visually-guided eye and arm movement behaviors.