Francesco Tenore

Decoding of Individuated Finger Movements Using Surface Electromyography

Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals. While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movements would require more elaborate control schemes. We show that it is possible to decode individual flexion and extension movements of each finger (ten movements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference ( p < 0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.

Restoring the sense of touch with a prosthetic hand through a brain interface.

Significance Our ability to manipulate objects relies fundamentally on sensory signals originating from the hand. To restore motor function with upper-limb neuroprostheses requires that somatosensory feedback be provided to the tetraplegic patient or amputee. Accordingly, we have developed approaches to convey sensory information critical for object manipulation—information about contact location, pressure, and timing—through intracortical microstimulation of somatosensory cortex. In experiments with nonhuman primates, we show that we can elicit percepts that are projected to a localized patch of skin, that track the pressure exerted on the skin, and that signal the timing of contact events. We anticipate that the proposed biomimetic feedback will constitute an important step in restoring touch to individuals who have lost it. Our ability to manipulate objects dexterously relies fundamentally on sensory signals originating from the hand. To restore motor function with upper-limb neuroprostheses requires that somatosensory feedback be provided to the tetraplegic patient or amputee. Given the complexity of state-of-the-art prosthetic limbs and, thus, the huge state space they can traverse, it is desirable to minimize the need for the patient to learn associations between events impinging on the limb and arbitrary sensations. Accordingly, we have developed approaches to intuitively convey sensory information that is critical for object manipulation—information about contact location, pressure, and timing—through intracortical microstimulation of primary somatosensory cortex. In experiments with nonhuman primates, we show that we can elicit percepts that are projected to a localized patch of skin and that track the pressure exerted on the skin. In a real-time application, we demonstrate that animals can perform a tactile discrimination task equally well whether mechanical stimuli are delivered to their native fingers or to a prosthetic one. Finally, we propose that the timing of contact events can be signaled through phasic intracortical microstimulation at the onset and offset of object contact that mimics the ubiquitous on and off responses observed in primary somatosensory cortex to complement slowly varying pressure-related feedback. We anticipate that the proposed biomimetic feedback will considerably increase the dexterity and embodiment of upper-limb neuroprostheses and will constitute an important step in restoring touch to individuals who have lost it.

Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals

The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98%. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.

Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons

Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of 3 actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.

Behavioral Demonstration of a Somatosensory Neuroprosthesis

Tactile sensation is critical for effective object manipulation, but current prosthetic upper limbs make no provision for delivering somesthetic feedback to the user. For individuals who require use of prosthetic limbs, this lack of feedback transforms a mundane task into one that requires extreme concentration and effort. Although vibrotactile motors and sensory substitution devices can be used to convey gross sensations, a direct neural interface is required to provide detailed and intuitive sensory feedback. In light of this, we describe the implementation of a somatosensory prosthesis with which we elicit, through intracortical microstimulation (ICMS), percepts whose magnitude is graded according to the force exerted on the prosthetic finger. Specifically, the prosthesis consists of a sensorized finger, the force output of which is converted into a regime of ICMS delivered to primary somatosensory cortex through chronically implanted multi-electrode arrays. We show that the performance of animals (Rhesus macaques) on a tactile task is equivalent whether stimuli are delivered to the native finger or to the prosthetic finger.

Continuous decoding of finger position from surface EMG signals for the control of powered prostheses

As development toward multi-fingered dexterous prosthetic hands continues, there is a growing need for more flexible and intuitive control schemes. Through the use of generalized electrode placement and well-established methods of pattern recognition, we have developed a basis for asynchronous decoding of finger positions. With the present method, correlations as large as 0.91 and mean overall decoding errors of ∼11% have been achieved with average decoding errors of between decoded and actual conformation of the metacarpophalangeal joints of individual fingers. It is hoped that these results will serve as a foundation from which to encourage further investigation into more intuitive methods of myoelectric control of powered upper limb prostheses.

Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.

CPG Design using Inhibitory Networks

We describe in detail the behavior of an inhibitory Central Pattern Generator (CPG) network for robot control. A four-neuron, mutual inhibitory network forms the basic coordinating pattern for locomotion. This network then inhibits an eight-neuron network used to drive patterned movement. We show that we can get predictable control of important relationships such as the phase of the hip and the knee by adjusting tonic parameters. We demonstrate the basic concept both in a simulation that is used to drive a trotting bipedal robot as well as an aVLSI CPG chip that generates spiking burst patterns. Our results indicate that an inhibitory framework can generate simple, understandable and flexible networks for legged robot control that can be implemented in custom VLSI circuits.

Low-cost electroencephalogram (EEG) based authentication

A low-cost, consumer-grade, EEG-based individual authentication system is proposed in this work. While EEG signals are recorded, the subject performs four mental imagery tasks consisting of a baseline measurement, referential limb movement, counting, and rotation for 150 seconds each. The 150 seconds of data are divided into one second segments, from which features are obtained. Three sets of features are extracted from each electrode: 6th order autoregressive (AR) coefficients, power spectral density, and total power in five frequency bands. Two additional sets of features are extracted from interhemispheric data: interhemispheric power differences and interhemispheric linear complexity. These feature sets are combined into a feature vector that is then used by a linear support vector machine (SVM) with cross validation for classification. The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs). Using voting rules across groups of ten segments, we were able to achieve 100% classification accuracy for each subject in each task. Though more work must be done with a larger subject pool as well as across multiple sessions, these results show that low-cost EEG authentication systems may be viable.

A Silicon Central Pattern Generator Controls Locomotion in Vivo

We present a neuromorphic silicon chip that emulates the activity of the biological spinal central pattern generator (CPG) and creates locomotor patterns to support walking. The chip implements ten integrate-and-fire silicon neurons and 190 programmable digital-to-analog converters that act as synapses. This architecture allows for each neuron to make synaptic connections to any of the other neurons as well as to any of eight external input signals and one tonic bias input. The chips functionality is confirmed by a series of experiments in which it controls the motor output of a paralyzed animal in real-time and enables it to walk along a three-meter platform. The walking is controlled under closed-loop conditions with the aide of sensory feedback that is recorded from the animals legs and fed into the silicon CPG. Although we and others have previously described biomimetic silicon locomotor control systems for robots, this is the first demonstration of a neuromorphic device that can replace some functions of the central nervous system in vivo.