Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats.

The use of electrical stimulation devices to manage bladder incontinence relies on the application of continuous inhibitory stimulation. However, continuous stimulation can result in tissue fatigue and increased delivered charge. Here, we employ a real-time algorithm to provide a short-time prediction of urine leakage using the high-resolution power spectrum of the bladder pressure during the presence of non-voiding contractions (NVC) in normal and overactive bladder (OAB) cats.

A Distributed Automatic Control Framework for Simultaneous Control of Torque and Cadence in Functional Electrical Stimulation Cycling.

One of the major challenges facing functional electrical stimulation (FES) cycling is the design of an automatic control system that addresses the problem of disturbance with unknown bound and time-varying behavior of the muscular system. The previous methods for FES-cycling are based on the system modeling and require pre-adjustment of the control parameters which are based on the model parameters. These will degrade the FES-cycling performance and limit the clinical application of the methods.

Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network.

To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats.

A general framework for automatic closed-loop control of bladder voiding induced by intraspinal microstimulation in rats.

Individuals with spinal cord injury or neurological disorders have problems in voiding function due to the dyssynergic contraction of the urethral sphincter. Here, we introduce a closed-loop control of intraspinal microstimulation (ISMS) for efficient bladder voiding. The strategy is based on asynchronous two-electrode ISMS with combined pulse-amplitude and pulse-frequency modulation without requiring rhizotomy, neurotomy, or high-frequency blocking.

Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion.

. The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord to decode the hindlimb kinematics during walking on a treadmill..

A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate.

Objective: In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey.

Approach: A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements.

Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network.

In this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) architecture, which has emerged as a general and effective model for capturing long-term temporal dependencies with good generalization performance. In this way, training the network with the data recorded from one rat could lead to estimating the bladder status of different rats.

A probabilistic recurrent neural network for decoding hind limb kinematics from multi-segment recordings of the dorsal horn neurons.

Objective: Providing accurate and robust estimates of limb kinematics from recorded neural activities is prominent in closed-loop control of functional electrical stimulation (FES). A major issue in providing accurate decoding the limb kinematics is the decoding model. The primary goal of this study is to develop a decoding approach to model the dynamic interactions of neural systems for accurate decoding.

Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information.

Introduction: In this paper, nonlinear dynamical analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics.

Methods: We use the RQA-based measures as the quantitative features of the nonlinear EEG dynamics.

Block-based robust control of stepping using intraspinal microstimulation.

Objective: The problem of motor control using intraspinal microstimulation (ISMS) can be approached at two levels of the motor system: individual muscles (motor pools) and motor primitives. The major challenges of direct ISMS at the level of individual muscle are the number of electrodes that are required to be implanted in order to recruit all muscles involving the motion and muscle selectivity. One solution to cope with these problems is the control of movement generated by appropriate combination of the movement primitives.

A piecewise probabilistic regression model to decode hand movement trajectories from epidural and subdural ECoG signals.

Objective: The primary concern of this study is to develop a probabilistic regression method that would improve the decoding of the hand movement trajectories from epidural ECoG as well as from subdural ECoG signals.

Approach: The model is characterized by the conditional expectation of the hand position given the ECoG signals. The conditional expectation of the hand position is then modeled by a linear combination of the conditional probability density functions defined for each segment of the movement.

Decoding hind limb kinematics from neuronal activity of the dorsal horn neurons using multiple level learning algorithm.

Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. So far, many attempts have been done to extract sensory information from dorsal root ganglia and sensory nerves. In this work, we examine decoding joint angles trajectories from the single-electrode extracellular recording of dorsal horn gray matter of the spinal cord during passive limb movement in anesthetized cats.

A modular robust control framework for control of movement elicited by multi-electrode intraspinal microstimulation.

Objective: An important issue in restoring motor function through intraspinal microstimulation (ISMS) is the motor control. To provide a physiologically plausible motor control using ISMS, it should be able to control the individual motor unit which is the lowest functional unit of motor control. By focal stimulation only a small group of motor neurons (MNs) within a motor pool can be activated.

The Effects of Stimulation Strategy on Joint Movement Elicited by Intraspinal Microstimulation.

The goal of this study was to characterize the effects of stimulation parameters and multielectrode stimulation on selectivity, range of motion, recruitment characteristics, and fatigue during intraspinal microstimulation (ISMS). A custom-made multielectrode array was implanted into the activation pool of the rat dorsiflexor muscle where the stimulation produced the highest movement range on the ankle joint and the least effect on the other joints. The results show that the selectivity could be significantly enhanced using multielectrode stimulation strategy.

Fully automatic control of paraplegic FES pedaling using higher-order sliding mode and fuzzy logic control.

In this paper, a fully automatic robust control strategy is proposed for control of paraplegic pedaling using functional electrical stimulation (FES). The method is based on higher-order sliding mode (HOSM) control and fuzzy logic control. In FES, the strength of muscle contraction can be altered either by varying the pulse width (PW) or by the pulse amplitude (PA) of the stimulation signal.

Fuzzy logic control of ankle movement using multi-electrode intraspinal microstimulation.

In this paper, we propose a fuzzy logic control (FLC) for control of ankle movement using multi-electrode intraspinal microstimulation (ISMS). It has been demonstrated that ISMS via multi-electrode implanted into a given motor pool has several advantages over the single-electrode ISMS. In the current study, we investigate the closed-loop control of ankle movement using multi-electrode ISMS.

Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI.

Dynamic optimization of walker-assisted FES-activated paraplegic walking: simulation and experimental studies.

In this paper, we propose a musculoskeletal model of walker-assisted FES-activated paraplegic walking for the generation of muscle stimulation patterns and characterization of the causal relationships between muscle excitations, multi-joint movement, and handle reaction force (HRF). The model consists of the lower extremities, trunk, hands, and a walker. The simulation of walking is performed using particle swarm optimization to minimize the tracking errors from the desired trajectories for the lower extremity joints, to reduce the stimulations of the muscle groups acting around the hip, knee, and ankle joints, and to minimize the HRF.

Restoring Motor Functions in Paralyzed Limbs through Intraspinal Multielectrode Microstimulation Using Fuzzy Logic Control and Lag Compensator.

In this paper, a control strategy is proposed for control of ankle movement on animals using intraspinal microstimulation (ISMS). The proposed method is based on fuzzy logic control. Fuzzy logic control is a methodology of intelligent control that mimics human decision making process.

A decentralized modular control framework for robust control of FES-activated walker-assisted paraplegic walking using terminal sliding mode and fuzzy logic control.

A major challenge to developing functional electrical stimulation (FES) systems for paraplegic walking and widespread acceptance of these systems is the design of a robust control strategy that provides satisfactory tracking performance. The systems need to be robust against time-varying properties of neuromusculoskeletal dynamics, day-to-day variations, subject-to-subject variations, external disturbances, and must be easily applied without requiring offline identification during different experimental sessions. Another major problem related to walker-assisted FES-activated walking concerns the high metabolic rate and upper body effort that limit the clinical applications of FES systems.

Adaptive neuro-fuzzy sliding mode control of multi-joint movement using intraspinal microstimulation.

During the last decade, intraspinal microstimulation (ISMS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoration of a desired functional limb movement through the use of ISMS is the development of a robust control strategy for determining the stimulation patterns. Accurate and stable control of limbs by functional intraspinal microstimulation is a very difficult task because neuromusculoskeletal systems have significant nonlinearity, time variability, large latency and time constant, and muscle fatigue.

Tree-structured feature extraction using mutual information.

One of the most informative measures for feature extraction (FE) is mutual information (MI). In terms of MI, the optimal FE creates new features that jointly have the largest dependency on the target class. However, obtaining an accurate estimate of a high-dimensional MI as well as optimizing with respect to it is not always easy, especially when only small training sets are available.

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.

Higher-order sliding mode control of leg power in paraplegic FES-cycling.

In this paper, we propose a robust control methodology based on high order sliding mode (HOSM) for control of the leg power in FES-Cycling. A major obstacle to the development of control systems for functional electrical stimulation (FES) has been the highly non-linear, time-varying properties of neuromusculoskeletal systems. A useful and powerful control scheme to deal with the uncertainties, nonlinearities, and bounded external disturbances is the sliding mode control (SMC).

Mutual information-based Fisher discriminant analysis for feature extraction and recognition with applications to medical diagnosis.

This paper presents a novel discriminant analysis (DA) for feature extraction using mutual information (MI) and Fisher discriminant analysis (MI-FDA). Most DA algorithms for feature extraction are based on a transformation which maximizes the between-class scatter and minimizes the within-class scatter. In contrast, the proposed method uses the Fisher's criterion to find a transformation that maximizes the MI between the transferred features and the target classes and minimizes the redundancy.