Application of Neural Prosthesis in Rehabilitation of Cervical Spinal Cord Injury (review) / 中国康复理论与实践

Neural prosthesis control system is based on brain-computer interface and functional electrical stimulation technology, by an-alyzing the electroencephalograph control commands directly into the muscle system or an external device, which compensated efferent pathway from the brain-spinal cord, and recovered motor function of patients with cervical spinal cord injury. This paper described the basic structure, working principle and key technology of neural prosthetic system, summarized the application, problems and prospects of neural prosthetic technology in the rehabilitation of cervical spinal cord injury.

Application of Diaphragm Pacing Technology in Respiratory Function Reconstruction in Patients with Cervical Spinal Cord Injury (review) / 中国康复理论与实践

The incidence of spinal cord injury is becoming higher and higher, of which more than half are cervical spinal injury. The main cause of death in high cervical spinal injury is respiratory function failure. The patients who survived must rely on ventilators to sus-tain life. In view of the shortcomings of ventilators, many researchers tried to use diaphragm pacing technology instead of ventilator to re-construct the function of respiratory. This article introduced the application of diaphragm pacing technology in patients with cervical spinal injury.

Application of Diaphragm Pacing Technology in Respiratory Function Reconstruction in Patients with Cervical Spinal Cord Injury (review) / 中国康复理论与实践

@#The incidence of spinal cord injury is becoming higher and higher, of which more than half are cervical spinal injury. The main cause of death in high cervical spinal injury is respiratory function failure. The patients who survived must rely on ventilators to sustain life. In view of the shortcomings of ventilators, many researchers tried to use diaphragm pacing technology instead of ventilator to reconstruct the function of respiratory. This article introduced the application of diaphragm pacing technology in patients with cervical spinal injury.

Application of Diaphragm Pacing Technology in Respiratory Function Reconstruction in Patients with Cervical Spinal Cord Injury (review) / 中国康复理论与实践

@#The incidence of spinal cord injury is becoming higher and higher, of which more than half are cervical spinal injury. The main cause of death in high cervical spinal injury is respiratory function failure. The patients who survived must rely on ventilators to sustain life. In view of the shortcomings of ventilators, many researchers tried to use diaphragm pacing technology instead of ventilator to reconstruct the function of respiratory. This article introduced the application of diaphragm pacing technology in patients with cervical spinal injury.

Electrically-evoked Neural Activities of rd1 Mice Retinal Ganglion Cells by Repetitive Pulse Stimulation

For successful visual perception by visual prosthesis using electrical stimulation, it is essential to develop an effective stimulation strategy based on understanding of retinal ganglion cell (RGC) responses to electrical stimulation. We studied RGC responses to repetitive electrical stimulation pulses to develop a stimulation strategy using stimulation pulse frequency modulation. Retinal patches of photoreceptor-degenerated retinas from rd1 mice were attached to a planar multi-electrode array (MEA) and RGC spike trains responding to electrical stimulation pulse trains with various pulse frequencies were observed. RGC responses were strongly dependent on inter-pulse interval when it was varied from 500 to 10 ms. Although the evoked spikes were suppressed with increasing pulse rate, the number of evoked spikes were >60% of the maximal responses when the inter-pulse intervals exceeded 100 ms. Based on this, we investigated the modulation of evoked RGC firing rates while increasing the pulse frequency from 1 to 10 pulses per second (or Hz) to deduce the optimal pulse frequency range for modulation of RGC response strength. RGC response strength monotonically and linearly increased within the stimulation frequency of 1~9 Hz. The results suggest that the evoked neural activities of RGCs in degenerated retina can be reliably controlled by pulse frequency modulation, and may be used as a stimulation strategy for visual neural prosthesis.

In vivo Performance Evaluation of Implantable Wireless Neural Signal Transmission System for Brain Machine Interface

A brain-machine interface (BMI) has recently been introduced to research a reliable control of machine from the brain information processing through single neural spikes in motor brain areas for paralyzed individuals. Small, wireless, and implantable BMI system should be developed to decode movement information for classifications of neural activities in the brain. In this paper, we have developed a totally implantable wireless neural signal transmission system (TiWiNets) combined with advanced digital signal processing capable of implementing a high performance BMI system. It consisted of a preamplifier with only 2 operational amplifiers (op-amps) for each channel, wireless bluetooth module (BM), a Labview-based monitor program, and 16 bit-RISC microcontroller. Digital finite impulse response (FIR) band-pass filter based on windowed sinc method was designed to transmit neural signals corresponding to the frequency range of 400 Hz to 1.5 kHz via wireless BM, measuring over -48 dB attenuated in the other frequencies. Less than +/-2% error by inputting a sine wave at pass-band frequencies for FIR algorithm test was obtained between simulated and measured FIR results. Because of the powerful digital FIR design, the total dimension could be dramatically reduced to 23x27x4 mm including wireless BM except for battery. The power isolation was built to avoid the effect of radio-frequency interference on the system as well as to protect brain cells from system damage due to excessive power dissipation or external electric leakage. In vivo performance was evaluated in terms of long-term stability and FIR algorithm for 4 months after implantation. Four TiWiNets were implanted into experimental animals' brains, and single neural signals were recorded and analyzed in real time successfully except for one due to silicon- coated problem. They could control remote target machine by classify neural spike trains based on decoding technology. Thus, we concluded that our study could fulfill in vivo needs to study various single neuron-movement relationships in diverse fields of BMI.

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.

Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.