Effect of stress on spatial working memory and EEG signal dynamics in the follicular and luteal phases of the menstrual cycle in young single girls.

AIM: Women undergo behavioral changes during the menstrual cycle. This study aimed to investigate the effect of estradiol (Es) on stress and effect of stress on spatial working memory (WM) and also to investigate electroencephalogram (EEG) signal's dynamics in the early and late follicular (EF and LF) and luteal (LU) phases of unmarried girls' menstrual cycle. METHODS: Stress was induced by presentation of a short (3 min) movie clip. Simultaneous with a memory test and stress induction, EEG, serum Es levels, and galvanic skin response (GSR) were assessed. RESULTS: Serum Es concentrations were decreased in LF, LU, and EF phases. The mean GSR score decreased after stress induction in all three phases, but it increased in the LF and LU phases versus the EF phase. Spatial WM diminished after stress induction in all three phases, but it increased in the LF phase versus the two phases before and after stress induction. Average power spectrum density in all frequency bands increased after stress induction in the frontal and prefrontal channels in the spatial WM test. CONCLUSION: The results showed that stress led to spatial WM dysfunction; however, Es improved spatial WM performance in the LF phase versus the other two phases.

Is the Hénon map able to predict the interaction dynamics between the knee and hip joints emerged during sit-to-stand movement?

In this study, the performance of a two-dimensional Hénon map in predicting the interactive dynamics of the knee and hip joints emerging during a normative sit-to-stand movement was evaluated. The instantaneous values of the knee and hip joints were the model inputs, and the next values of the knee and hip joints were predicted by the Hénon map. The map predicted the desired relative behavior of the joints, showing synergetic coordination between the joints. The experimental data were recorded from four healthy participants and used to identify the Hénon map via a genetic algorithm. Model performance was quantitatively assessed by computing the calculated prediction error and analyzing the behavioral dynamics of the state spaces reconstructed via the captured kinematic data. According to the results, there was an obvious similarity between the dynamics of the state space trajectories of the identified model and those of the recorded data, not only in terms of stretching and folding dynamics, but also concerning generalized synchrony. The acceptable performance of the proposed modeling solution can also be demonstrated through these results.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

Comments on "Synergy-Based Functional Electrical Stimulation for Poststroke Rehabilitation of Upper-Limb Motor Functions".

Despite the interesting innovation proposed in the paper, "Synergy-based functional electrical stimulation for poststroke rehabilitation of upper-limb motor functions," concerning the design of functional electrical stimulation (FES) profile, we are skeptical regarding the genuine effectiveness of the applied rehabilitation strategy. In this note, we argue that applying the rehabilitation method proposed in the above-noted work cannot pave the way for eliciting a motor learning process. Consequently, the proposed method cannot be regarded as a FES-based rehabilitation approach for poststroke rehabilitation of upper-limb motor functions.

The effect of training on variability and accuracy of overhand throwing in children with Down syndrome.

OBJECTIVE: People with Down syndrome (DS) have higher variability in their motor skills compared to other counterparts without intellectual disability. Given that the effect of physical training on the variability and accuracy is unclear, the purpose of this study was to examine the effect of nine sessions of overhand throwing training on the variability and accuracy of overhand throwing in children with DS. METHODS: Twenty-seven children with DS randomly assigned to experimental and control groups. In the pretest, two groups threw the tennis ball three times to the fixed target. Absolute error (AE) and the normalized root mean square (NoRMS) of segmental couplings of shoulder-elbow (NoRMS 1) and elbow-wrist (NoRMS 2) calculated from the scores of throwing and kinematics data, respectively. Then the experimental group participated in overhand throwing training for nine sessions. After that, the post-test was taken and ten days later, the retention test performed with pretest conditions. RESULTS: The results of two-way ANOVA with repeated measures on AE values showed the experimental group in the post-test and retention phases was more accurate than the control group. Also, AE values of the experimental group in the post-test and retention phases were significantly lower than the pretest. The results of two-way ANOVA with repeated measures showed that NoRMS 1 in the experimental group was significantly lower than control group in the post-test and retention phases. CONCLUSION: It seems the overhand throwing training can reduce the variability and increase the accuracy of overhand throwing in children with DS.

A Hybrid-FES Based Control System for Knee Joint Movement Control.

INTRODUCTION: Utilizing Functional Electrical Stimulation (FES) and rehabilitation robots for motion control is an open research problem. In this paper, a new control algorithm has been proposed which was de-signed based on a combination of FES and an active mechanical actuator to control the knee joint movement. METHODS: An adaptive controller and a Proportional-Derivative (PD) controller have adjusted the motor torque and stimulation intensity, respectively. The FES controller was activated whenever a disturbance observer detected the presence of the external disturbance. In this manner, the occurrence of the muscle fatigue arises from the FES can be postponed. RESULTS: The simulation studies were carried out on a model of muscle-joint system along with a model of a servo-motor. The computed RMS of the tracking errors compared to the range of knee motion show that the tracking performance is acceptable. In this research, the trajectories envisioned as the knee joint reference trajectory were designed using the recorded human data. CONCLUSION: The achieved results prove the ability of the proposed control strategy to not only reject the external disturbance but also compensate the muscle fatigue.

Evaluating the Effect of Parkinson's Disease on Jitter and Shimmer Speech Features.

BACKGROUND: Parkinson's disease (PD) is a neurological disorder caused by decreasing dopamine in the brain. Speech is one of the first functions that are disrupted. Accordingly, speech features are a promising indicator in PD diagnosis for telemedicine applications. The purpose of this study is to investigate the impact of Parkinson's disease on a minimal set of Jitter and Shimmer voice indicators and studying the difference between male and female speech features in noisy/noiseless environments. MATERIALS AND METHODS: Our data includes 47 samples from nursing homes and neurology clinics, with 23 patients and 24 healthy individuals. The optimal feature for each category is studied separately for the men's and women's samples. The focus here is on the phonation in which the vowel/a/is expressed by the participants. The main features, including Jitter and Shimmer perturbations, are extracted. To find an optimal pair under both noisy and noiseless circumstance, we use the Relief feature selection strategy. RESULTS: This research shows that the Jitter feature for men and women with Parkinson's is 21 and 33.4, respectively. While the Shimmer feature is 0.1 and 0.06. In addition, by using these two features alone, we reach a correct diagnosis rate of 79% and 81% for noisy and noiseless states, respectively. CONCLUSION: The PD effects on the speech features can be accurately identified. Evaluating the extracted features suggests that the absolute value of the selected feature in men with PD is higher than for healthy ones. Whereas, in the case of women, this is the opposite.

Human Gait Control Using Functional Electrical Stimulation Based on Controlling the Shank Dynamics.

INTRODUCTION: Efficient gait control using Functional Electrical Stimulation (FES) is an open research problem. In this research, a new intermittent controller has been designed to control the human shank movement dynamics during gait. METHODS: In this approach, first, the three-dimensional phase space was constructed using the human shank movement data recorded from the healthy subjects. Then, three iterated sine-circle maps were extracted in the mentioned phase space. The three identified one-dimensional maps contained the essential information about the shank movement dynamics during a gait cycle. Next, an intermittent fuzzy controller was designed to control the shank angle. According to the adopted intermittent control strategy, the fuzzy controller is activated whenever the shank angle is far enough from the specific. The specific points are described using the identified iterated maps in the constructed phase space. In this manner, the designed controller is activated during a short-time fraction of the gait cycle time. RESULTS: The designed intermittent controller was evaluated through some simulation studies on a two-joint musculoskeletal model. The obtained results suggested that the pattern of the obtained hip and knee joint trajectories, the outputs of the musculoskeletal model, were acceptably similar to the joints' trajectories pattern of healthy subjects. CONCLUSION: The intriguing similarity was observed between the dynamics of the recorded human data and those of the controlled musculoskeletal model. It supports the acceptable performance of the proposed control strategy.

A chaos to chaos control approach for controlling the chaotic dynamical systems using Hamilton energy feedback and fuzzy-logic system.

In this article, energy-based feedback control is introduced merely as an approach to suppress chaos. We have also shown in this study that an energy-based feedback controller is capable of changing a chaotic dynamic to other chaotic dynamics. In other words, energy feedback can also be used to convert chaos dynamics to another chaos dynamics, and the use of energy feedback should not be limited to suppress chaos. The importance of the issue lies in relating some practical applications of chaos to chaos control. In this short study, we have shown that an energy feedback control can be combined with a fuzzy self-regulating gain system. A short study has been done on Chua's circuit.

Pre-programming in overhand throwing of children with Down syndrome: role of the generalized motor program.

OBJECTIVE: It is unclear whether slowness and higher reaction time of individuals with Down syndrome (DS) are because of inability to pre-program and using the generalized motor program (GMP) or not. So, the purpose of this study was to examine the consistency of relative timing as a fixed feature of a GMP in overhand throwing with changing the distance to target as a varied feature. METHODS: Fifteen individuals with DS (age = 13 ± 2 y) and a control group of 12 individuals without intellectual disability (ID; age = 13 ± 2 y) were asked to throw a tennis ball to a fixed target from three distances of 2, 2.75, and 3.25 m, respectively. Instant of occurrence of the following discrete variables was recorded by motion analysis: initiation of elbow extension, maximum shoulder angular velocity, maximum elbow angular velocity, and maximum resultant hand velocity. RESULTS: Results of two-way analysis of variance test did not show any significant difference in any of the relative kinematic variables in distances and groups (p > 0.05). CONCLUSION: It seems that individuals with DS are able to motor preprogram and they use a GMP to overhand throwing from different distances as well as those without ID. Also, slowness and reaction time are unrelated to pre-programming and GMP as it relates to overhand throwing.

Generating the Visual Biofeedback Signals Applicable to Reduction of Wrist Spasticity: A Pilot Study on Stroke Patients.

INTRODUCTION: Application of biofeedback techniques in rehabilitation has turned into an exciting research area during the recent decade. Providing an appropriate visual or auditory biofeedback signal is the most critical requirement of a biofeedback technique. In this regard, changes in Surface Electromyography (SEMG) signals during wrist movement can be used to generate an indictable visual biofeedback signal for wrist movement rehabilitation via SEMG biofeedback. This paper proposes a novel methodology for selecting the most appropriate features out of wrist muscle SEMG signals. METHODS: To this end, the surface EMG signals from flexor and extensor muscle groups during wrist joint movements were recorded and analyzed. Some linear and nonlinear features in frequency, time, and time-frequency domains were extracted from the recorded surface EMG signals of the flexor and extensor muscles. Experiments and analyses were performed on ten healthy subjects and four stroke patients with wrist muscle spasticity as the movement disorder subjects. Some heuristic feature selection measures were applied. The main motivation behind choosing applied heuristic feature selection measures was meeting. In the first step, the designed visual biofeedback signal should indicate a healthy wrist motion profile as its successful tracking by the patient guarantees rehabilitation. In addition, the visual biofeedback signal should be a smooth curve thus preventing the patient from discomfort while tracking it on a monitor during the biofeedback therapy. RESULTS: In this pilot study, after using the introduced feature selection measures, quantitative and qualitative analyses of the extracted features indicated that Shannon entropy is the most appropriate feature for generating a visual biofeedback signal as a healthy wrist motion profile to improve the ability of stroke patients in controlling wrist joint motion. In addition, it was shown that when the wrist joint moves between a flexed and rest position, the flexor muscle EMG signal should be used for generating a visual biofeedback signal. However when the wrist joint moves between a rest position and an extended position, the extensor muscle EMG signal is appropriate for providing a visual biofeedback signal. It is worth noting that the achieved pilot study results should be confirmed by the future studies with larger samples. CONCLUSION: According to the obtained results, it can be concluded that among the analyzed features, the Shannon entropy was the most appropriate feature. It can be employed for generating a visual biofeedback signal for reduction of spasticity in patients with stroke.

Prediction of the Wrist Joint Position During a Postural Tremor Using Neural Oscillators and an Adaptive Controller.

The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators' output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable.

A decentralized adaptive fuzzy robust strategy for control of upright standing posture in paraplegia using functional electrical stimulation.

In this paper, we present a novel decentralized robust methodology for control of quiet upright posture during arm-free paraplegic standing using functional electrical stimulation (FES). Each muscle-joint complex is considered as a subsystem and individual controllers are designed for each one. Each controller operates solely on its associated subsystem, with no exchange of information between them, and the interaction between the subsystems are taken as external disturbances. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed. The method is based on the synergistic combination of an adaptive nonlinear compensator with sliding mode control (SMC). Fuzzy logic system is used to represent unknown system dynamics for implementing SMC and an adaptive updating law is designed for online estimating the system parameters such that the global stability and asymptotic convergence to zero of tracking errors is guaranteed. The proposed controller requires no prior knowledge about the dynamics of system to be controlled and no offline learning phase. The results of experiments on three paraplegic subjects show that the proposed control strategy is able to maintain the vertical standing posture using only FES control of ankle dorsiflexion and plantarflexion without using upper limbs for support and to compensate the effect of external disturbances and muscle fatigue.

A decentralized adaptive robust method for chaos control.

This paper presents a control strategy, which is based on sliding mode control, adaptive control, and fuzzy logic system for controlling the chaotic dynamics. We consider this control paradigm in chaotic systems where the equations of motion are not known. The proposed control strategy is robust against the external noise disturbance and system parameter variations and can be used to convert the chaotic orbits not only to the desired periodic ones but also to any desired chaotic motions. Simulation results of controlling some typical higher order chaotic systems demonstrate the effectiveness of the proposed control method.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles.

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.