Resonant dynamic Stark shift as a tool in strong-field quantum control: calculation and application for selective multiphoton ionization of sodium.

A method for determining the resonant dynamic Stark shift (RDSS), based on wave-packet calculations of the populations of quantum states, is presented. It is almost insensitive to variations of the laser pulse profile, and this feature ensures generality in applications. This method is used to determine an RDSS data set for 3s → nl (n ≤ 6) transitions in sodium induced by laser pulses with peak intensities up to 7.9 × 1012 W cm-2 and wavelengths in the range from 455.6 to 1139 nm. The data are applied to analyze the photoelectron spectra (electron yield versus excess energy) of the sodium atom interacting with 800 nm laser radiation. Substructures observed in the experimentally measured spectra are successfully reproduced and related to the resonantly enhanced multiphoton ionization via specific (P and F) intermediate states.

Recording and assessment of evoked potentials with electrode arrays.

In order to optimize procedure for the assessment of evoked potentials and to provide visualization of the flow of action potentials along the motor systems, we introduced array electrodes for stimulation and recording and developed software for the analysis of the recordings. The system uses a stimulator connected to an electrode array for the generation of evoked potentials, an electrode array connected to the amplifier, A/D converter and computer for the recording of evoked potentials, and a dedicated software application. The method has been tested for the assessment of the H-reflex on the triceps surae muscle in six healthy humans. The electrode array with 16 pads was positioned over the posterior aspect of the thigh, while the recording electrode array with 16 pads was positioned over the triceps surae muscle. The stimulator activated all the pads of the stimulation electrode array asynchronously, while the signals were recorded continuously at all the recording sites. The results are topography maps (spatial distribution of evoked potentials) and matrices (spatial visualization of nerve excitability). The software allows the automatic selection of the lowest stimulation intensity to achieve maximal H-reflex amplitude and selection of the recording/stimulation pads according to predefined criteria. The analysis of results shows that the method provides rich information compared with the conventional recording of the H-reflex with regard the spatial distribution.

Synergistic control of forearm based on accelerometer data and artificial neural networks.

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Neuromodulation of lower limb monoparesis: functional electrical therapy of walking.

After Cerebro-Vascular Accident (CVA), restoration of normal function, such as locomotion, depends on reorganization of existing central nervous system (CNS) circuitry. This capacity for reorganization, generally referred to as plasticity, is thought to underlie many instances of functional recovery after injury as well as learning and memory in the undamaged CNS. Both the reorganization of the supraspinal and spinal circuitry are highly important for the recovery of walking. The neural mechanisms responsible for learning and adapting processes are thought to involve changes both in the efficacy of synaptic function and the pattern of synaptic connections within neural circuits. In the uninjured CNS, these changes occur as a result of alterations in the amount of neural activity within circuits and are, therefore, termed activity-dependent. In this chapter, we will present several therapies of walking that provide effective input for the training of the existing CNS circuitry; thereby, contribute to long term recovery of sensory-motor functions. The focus of this chapter is Functional Electrical Therapy (FET) of walking, that is, the multi-channel electrical stimulation of sensory-motor systems that lead to more normal stance and swing of the paretic leg during the walking exercise.

Symmetry of post-movement beta-ERS and motor recovery from stroke: a low-resolution EEG pilot study.

The inter-hemispheric symmetry of electroencephalographic (EEG) post-movement beta-event-related synchronization (PMBS) after movements on a drawing board was studied in eight acute stroke subjects with mild hemiparesis and eight normal subjects. A follow-up testing was conducted 3 months after the initial recordings with a twofold purpose: (1) to validate the reproducibility of the experimental protocol in normal subjects; and (2) to study changes of inter-hemispheric PMBS-symmetry as a response to recovery of motor function. PMBS values were calculated and their topographic distributions illustrated at various time instances following movement offset. Significant PMBS patterns were present in all normal subjects, with only minor differences within consecutive recordings. The side of hemiparesis in acute stroke subjects could be distinguished (P = 0.04) on the basis of the signed symmetry index, a quantitative measure of lateralization. The follow-up testing on three recovered stroke subjects revealed a trend of changes in the lateralization towards the contralateral side of movement, an indication that the cortical organization of movement following recovery turned out as reported for normal subjects. Further clinical investigations need to be carried out to evaluate the relationship between recovery and PMBS symmetry on a large number of subjects, using the method presented here.

New controller for functional electrical stimulation systems.

A novel, self-contained controller for functional electrical stimulation systems has been designed. The development was motivated by the need to have a general purpose, easy to use controller capable of stimulating many muscle groups, thus restoring complex motor functions (e.g. standing, walking, reaching, and grasping). The designed controller can regulate the frequency, pulse duration, and charge balance on up to 16 channels, and execute pre-programmed and sensory-driven control operations. The controller supports up to eight analog and six digital sensors, and comprises a memory block for including history of the sensory data (time series). Five independent timers provide the basis for the multi-modal and multi-level control of movement. The PC compatible interface is realised via an IR serial communication channel. The PC based software is user friendly and fully menu driven. This paper also presents a case study where the controller was implemented to restore walking in a paraplegic subject. The assistive system comprised the novel controller, the power and output stages of an eight-channel FES system (IEEE Trans Rehabil Eng, TRE-2 (1994) 234), ankle-foot orthoses, and a rolling walker. Stimulation was applied with surface electrodes positioned over the motoneurons that innervate muscles responsible for the hip and knee flexion and extension. The sensory system included goniometers at knee and hip joints, force-sensing resistors built in the shoe insoles, and digital accelerometers at the hips. A rule-based control algorithm was generated following a two-step procedure: (1) simulation and (2) machine learning as described in earlier studies (IEEE Trans Rehab Eng, TRE-7 (1999) 69). The paraplegic subject walked faster, and with less physiological effort, when automatic control was applied as compared to hand-control. This case study, as well as a previous one for assisting grasping (The design and testing of a new programmable electronic stimulator. N. Fisekovic, MS thesis. University of Belgrade, Belgrade, 2000) indicate that the novel control unit is effectively applicable to FES systems.

Predicting quadriceps muscle activity during gait with an automatic rule determination method.

It has been suggested that control using a skill-based expert system can be applicable to gait restoration. Rule-based systems have several advantages for this application: they generate a fast response (they are not computationally intensive) and they are easy to comprehend and implement. A major problem with using such systems is the inability of users to determine its rules. In this study, an automatic method for obtaining the production rules from a set of examples is described. The rule base was automatically induced from a model which used external sensor signals as inputs and electromyogram (EMG) patterns as outputs. The method is based on the minimization of entropy. A production rule estimated the muscle activity pattern using the sensor information. The algorithm was tested using data recorded from six able-bodied individuals during ground level walking, with and without ankle-foot orthoses. The data showed that gait variability will increase in able-bodied subjects when the motion of ankle joints is restricted, thus, providing a good test for generalization. The experimental results illustrate performance of the production rule that estimates quadriceps muscle group activity pattern for ground level walking in able-bodied subjects.

Estimating mechanical parameters of leg segments in individuals with and without physical disabilities.

Methods are described for estimating the inertia, viscosity, and stiffness of the lower leg around the knee and of the whole leg around the hip that are applicable even to persons with considerable spasticity. These involve: 1) a "pull" test in which the limb is slowly moved throughout its range of motion while measuring angles (with an electrogoniometer) and torques (with a hand-held dynamometer) to determine passive stiffness and 2) a "pendulum" test in which the limb is moved against gravity and then dropped, while again measuring angles and torques. By limiting the extent of the movement and choosing a direction (flexion or extension) that minimizes reflex responses, the mechanical parameters can be determined accurately and efficiently using computer programs. In the sample of subjects studied (nine with disability related to spinal cord injury, head injury, or stroke, and nine with no neurological disability), the inertia of the lower leg was significantly reduced in the subjects with disability (p < 0.05) as a result of atrophy, but the stiffness and viscosity were within normal limits. The values of inertia were also compared with anthropometric data in the literature. The identification of these passive parameters is particularly important in designing systems for functional electrical stimulation of paralyzed muscles, but the methods may be widely applicable in rehabilitation medicine.

A novel electric design for electromagnetic stimulation--the Slinky coil.

A novel coil design for inductive electromagnetic stimulation of neural cells has been simulated and experimentally tested. This coil improves the focal effect of a magnetic stimulator, and it reduces its inductance, hence the efficiency of the system is improved. The basic structure of the device is derived from the popular "Slinky" toy. The actual device is formed by winding different numbers of loops forming a helical coil on a half torus. The loops are bunched at the axis of the torus. The coil, due to its geometry, generates a unique distribution of eddy currents in nearby tissues which is favorable compared to a solenoid type stimulator. This renders the Slinky coil more selective than conventional coils used for magnetic stimulation. The distribution of eddy currents was analyzed using Matlab, following Faraday's Law of Induction. Improved focality permits the current through the coil to be reduced for the same effect. In addition, the reduced inductance of the Slinky coil decreases the power requirement; thus, the improved efficiency of the system may allow the generation of bursts of pulses, and expand the utilization of the system to possible functional activation of certain neuro-muscular structures when peripheral nerves are stimulated.

Machine learning in control of functional electrical stimulation systems for locomotion.

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) [1] and the inductive learning algorithm (IL) [2]. Results to date suggest that, given the same training data, the IL learned faster than the ALN, while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified.

Instrumentation for ENG and EMG recordings in FES systems.

An electronic circuit for analog processing of neural (electroneurogram or ENG) and muscular (electromyogram or EMG) signals in functional electrical stimulation (FES) systems is described in this paper. The basic circuit consists of a low-noise gated preamplifier, band-pass filter, amplifier, and a blanking circuit to minimize stimulation artifacts during electrical stimulation. This device was tested in chronic recordings using a triphasic cuff electrode for nerves and epimysial electrodes for muscles in the hind limbs of cats. The device was used for nerve recordings in the presence of electrical stimulation of muscles in the same leg. The recordings showed rejection of stimulation and muscle (M-wave) artifacts, while retaining the information of interest.

Sensory nerve recording for closed-loop control to restore motor functions.

A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation we designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. We used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed-loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.

Electrical systems for improving locomotion after incomplete spinal cord injury: an assessment.

Simple systems for electrical stimulation (1-4 channels) with either surface, percutaneous, or implanted electrodes during locomotion were assessed in 10 subjects who had chronic, incomplete spinal cord injury (SCI). On average, the speed of locomotion was increased by 4 m/min independently of the subject's speed of locomotion without stimulation (0-50 m/min) while oxygen consumption was reduced somewhat. These simple systems can provide practical help, particularly for incomplete SCI subjects who can stand but are lacking or have very limited ability to walk. Further improvement in locomotion requires stabilization and reduction in the duration of the stance phase of locomotion.

Output space tracking control for above-knee prosthesis.

The control of a knee joint in an active above-knee prosthesis has been designed using the Lyapunov tracking method. A simulation of locomotion was done to prove that the tracking control in output space is a valuable real time control method for artificial legs. The data used for simulation was collected in able-bodied subjects while they walked on a powered treadmill. Human volunteers were braced with an ankle splint (limiting dorsi- and plantar flexion) and with a knee cage (limiting knee movements to the lateral plane). We studied the achieved tracking of the prescribed knee motion, deviations of the thigh movement from the prescribed trajectory, maximal angular deviations from the desired trajectory and the power consumption as functions of a limited maximal knee torque and a damping constant in the knee actuator. We found that the use of output tracking method is suitable for the design of appropriate hardware of an above-knee prosthesis and for real-time control.

Finite state model of locomotion for functional electrical stimulation systems.

A finite state model of locomotion was developed to simplify a controller design for motor activities of handicapped humans. This paper presents a model developed for real time control of locomotion with functional electrical stimulation (FES) assistive systems. Hierarchical control of locomotion was adopted with three levels: voluntary, coordination and actuator level. This paper deals only with coordination level of control. In our previous studies we demonstrated that a skill-based expert system can be used for coordination level of control in multi-joint FES systems. Basic elements in this skill-based expert system are production rules. Production rules have the form of If-Then conditional expressions. A technique of automatic determination of these conditional expressions is presented in this paper. This technique for automatic synthesis of production rules uses fuzzy logic and artificial neural networks (ANN). The special class of fuzzy logic elements used in this research is called preferential neurons. The preferential neurons were used to estimate the relevance of each of the sensory inputs to the recognition of patterns defined as finite states. The combination of preferential neurons forms a preferential neural network. The preferential neural network belongs to a class of ANNs. The preferential neural network determined the set of finite states convenient for a skill-based expert system for different modalities of locomotion.