A biomimetic controller for a multifinger prosthesis.

A novel controller for a multifinger hand prosthesis was developed and tested to measure its accuracy and performance in transducing volitional signals for individual "phantom" fingers. Pneumatic sensors were fabricated from open-cell polymeric foam, and were interposed between the prosthetic socket and superficial extrinsic tendons associated with individual finger flexion. Test subjects were prompted to move individual fingers or combinations thereof to execute either taps or grasps. Sensor outputs were processed by a computer that controlled motions of individual fingers on a mechanical prosthesis. Trials on three upper-limb amputees showed that after brief training sessions, the TAP controller was effective at producing voluntary flexions of individual fingers and grasping motions. Signal energies were between 5 and 25 dB relative to noise from all sources, including adjacent sensors, indicating high degrees of both sensitivity and specificity for tendon-associated transduction. Finger flexions at up to three repetitions per second, and rhythmic tapping of sequential fingers were readily transduced. One amputee subject was able to play a short piano piece with three fingers, at approximately one-quarter normal tempo. TAP sensors responded linearly to graded forces from individual fingers, indicating proportional force control. Our results demonstrate the feasibility of restoring some degree of finger dexterity by noninvasive sensing of extrinsic tendons.     

Fuzzy EMG classification for prosthesis control.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.     

A novel speech controller for radio amateurs with a vision impairment.

This paper describes a portable speech controller system for persons with a vision impairment to adjust the channel frequency of a radio set via speech commands. The speech commands are recognized on a general-purpose digital signal processor using a hidden Markov model (HMM), and are used to remotely control radio channel changes.     

The Neurochip BCI: towards a neural prosthesis for upper limb function.

The Neurochip BCI is an autonomously operating interface between an implanted computer chip and recording and stimulating electrodes in the nervous system. By converting neural activity recorded in one brain area into electrical stimuli delivered to another site, the Neurochip BCI could form the basis for a simple, direct neural prosthetic. In tests with normal, unrestrained monkeys, the Neurochip continuously recorded activity of single neurons in primary motor cortex for several weeks at a time. Cortical activity was correlated with simultaneously-recorded electromyogram (EMG) activity from arm muscles during free behavior. In separate experiments with anesthetized monkeys, we found that microstimulation of the cervical spinal cord evoked movements of the arm and hand, often involving multiple muscles synergies. These observations suggest that spinal microstimulation controlled by cortical neurons could help compensate for damaged corticospinal projections.     

Learning prosthetic vision: a virtual-reality study.

Acceptance of prosthetic vision will be heavily dependent on the ability of recipients to form useful information from such vision. Training strategies to accelerate learning and maximize visual comprehension would need to be designed in the light of the factors affecting human learning under prosthetic vision. Some of these potential factors were examined in a visual acuity study using the Landolt C optotype under virtual-reality simulation of prosthetic vision. Fifteen normally sighted subjects were tested for 10-20 sessions. Potential learning factors were tested at p < 0.05 with regression models. Learning was most evident across-sessions, though 17% of sessions did express significant within-session trends. Learning was highly concentrated toward a critical range of optotype sizes, and subjects were less capable in identifying the closed optotype (a Landolt C with no gap, forming a closed annulus). Training for implant recipients should target these critical sizes and the closed optotype to extend the limit of visual comprehension. Although there was no evidence that image processing affected overall learning, subjects showed varying personal preferences.     

Feedback error learning neural network for trans-femoral prosthesis.

Feedback-error learning (FEL) neural network was developed for control of a powered trans-femoral prosthesis. Nonlinearities and time-variations of the dynamics of the plant, in addition to redundancy and dynamic uncertainty during the double support phase of walking, makes conventional control methods very difficult to use. Rule-based control, which uses a knowledge base determined by machine learning and finite automata method is limited since it does not respond well to perturbations and environmental changes. FEL can be regarded as a hybrid control, because it combines nonparametric identification with parametric modeling and control. This paper presents simulation of a powered trans-femoral prosthesis controlled by a FEL neural network. Results suggest that FEL can be used to identify inverse dynamics of an arbitrary trans-femoral prosthesis during simple single joint movements (e.g., sinusoidal oscillations). The identified inverse dynamics then allows the tracking of an arbitrary trajectory such as a desired walking pattern within a multijoint structure. Simulation shows that the identified controller responds correctly when the leg motion is exposed to a perturbation such as a frequent change of the ground reaction force or the hip joint torque generated by the user. FEL eliminates the need for precise, tedious, and complex identification of model parameters.     

Biomechanical evaluation of a prototype foot/ankle prosthesis.

In this paper, we report on our pilot evaluation of a prototype foot/ankle prosthesis. This prototype has been designed and fabricated with the intention of providing decreased ankle joint stiffness during the middle portion of the stance phase of gait, and increased (i.e., more normal) knee range of motion during stance. Our evaluation involved fitting the existing prototype foot/ankle prosthesis, as well as a traditional solid ankle cushioned heel (SACH) foot, to an otherwise healthy volunteer with a below-knee (BK) amputation. We measured this individual's lower extremity joint kinematics and kinetics during walking using a video motion analysis system and force platform. These measurements permitted direct comparison of prosthetic ankle joint stiffness and involved side knee joint motion, as well as prosthetic ankle joint moment and power.     

Cortically coupled computer vision for rapid image search.

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.     

Moving towards a fuel cell vision for the UK

A new national initiative, Fuel Cells UK, has been established to act as a focus for the development of a fuel cell industry in Britain, and to liaise and partner more effectively with national and regional initiatives around the world. The first step is to create a ‘unified vision’ for the UK fuel cell sector, for both the short term and the long term, to guide industry, academia and government on the way to a world-class fuel cell industry.     

Classification of finger activation for use in a robotic prosthesis arm

Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier. This method results in a probability of error of less than 2%.     

Improved myoelectric prosthesis control using targeted reinnervation surgery: a case series.

Targeted reinnervation is a surgical technique developed to increase the number of myoelectric input sites available to control an upper-limb prosthesis. Because signals from the nerves related to specific movements are used to control those missing degrees-of-freedom, the control of a prosthesis using this procedure is more physiologically appropriate compared to conventional control. This procedure has successfully been performed on three people with a shoulder disarticulation level amputation and three people with a transhumeral level amputation. Performance on timed tests, including the box-and-blocks test and clothespin test, has increased two to six times. Options for new control strategies are discussed.     

Compliant grasp in a myoelectric hand prosthesis

A novel myoelectric hand prosthesis consisting of electromyogram (EMG) signal processing units, a microprocessor-based dc motor servo system, and a 1 degree-of-freedom (DOF) end effector has been developed. The flexion angle and compliance of the finger of this prosthesis can be voluntarily controlled with EMG signals. The biomimetic controller for the myoelectric hand incorporated a model of the neuromuscular control system constructed from an analysis of the force response to length perturbation of the flexor pollicis longus muscle, processing of EMG signals, and the configuration of the hand. Basic functions of the human neuromuscular control system are realized by using position control, force feedback, and variable gain, modulated by EMG signal amplitude. A limb-absent person and four healthy subjects were able to voluntarily control the finger angle and compliance of the prosthesis and were able to easily grasp a soft object after a short training period.     

Connectivity analysis as a novel approach to motor decoding for prosthesis control

The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r(2)) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.     

Automatic determination of synergies by radial basis function artificial neural networks for the control of a neural prosthesis.

This paper describes an automatic method for synthesizing the control for a neural prosthesis (NP) that could augment elbow flexion/extension and forearm pronation/supination in persons with hemiplegia. The basis for the control was a synergistic model of reaching and grasping that uses temporal and spatial synergies between the arm and body segments. The synergies were determined from the movement data measured in nondisabled persons during the performance of functional tasks. The work space was divided into six zones: distance (two attributes) and laterality (three attributes). Radial basis function artificial neural networks (RBF ANN) were used to determine synergies. Sets of RBF ANN characterized with good generalization were selected as control laws for elbow flexion/extension and forearm pronation/supination. The validation was performed for three categories: inter-subject, distance, and laterality generalization. For all of the defined spatial synergies, the correlation was high for inter-subject and distance, yet low for the laterality scenario. This suggests the necessity for implementing different maps for different directions, but the same maps for different distances. The natural movements of the upper arm then drive the lower arm (elbow flexion/extension and forearm pronation/supination) in a way that is very well suited for the administration of functional electrical therapy (FET) in persons with hemiplegia soon after the onset of impairment.     

Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex

Damage to a cortical area reduces not only information transmitted to other cortical areas, but also activation of these areas. This phenomenon, whereby the dynamics of a follower area are dramatically altered, is typically manifested as a marked reduction in activity. Ideally, neuroprosthetic stimulation would replace both information and activation. However, replacement of activation alone may be valuable as a means of restoring dynamics and information processing of other signals in this multiplexing system. We used neuroprosthetic stimulation in a computer model of the cortex to repair activation dynamics, using a simple repetitive stimulation to replace the more complex, naturalistic stimulation that had been removed. We found that we were able to restore activity in terms of neuronal firing rates. Additionally, we were able to restore information processing, measured as a restoration of causality between an experimentally recorded signal fed into the in silico brain and a cortical output. These results indicate that even simple neuroprosthetics that do not restore lost information may nonetheless be effective in improving the functionality of surrounding areas of cortex.