Epidural spinal cord stimulation (ESCS) combined with partial weight-bearing therapy (PWBT) has been shown to facilitate recovery of functional walking for individuals after spinal cord injury (SCI). The investigation of neural mechanisms of recovery from SCI under this treatment has been conducted broadly in rodent models, yet a suitable ESCS system is still unavailable. This paper describes a practical, programmable, and fully implantable stimulator for laboratory research on rats to explore fundamental neurophysiological principles for functional recovery after SCI. The ESCS system is composed of a personal digital assistant (PDA), an external controller, an implantable pulse generator (IPG), lead extension, and stimulating electrodes. The stimulation parameters can be programmed and adjusted through a graphical user interface on the PDA. The external controller is placed on the rat back and communicates with the PDA via radio-frequency (RF) telemetry. An RF carrier from the class-E power amplifier in the external controller provides both data and power for the IPG through an inductive link. The IPG is built around a microcontroller unit to generate voltage-regulated pulses delivered to the bipolar electrode for ESCS in rats. The encapsulated IPG measures 22 mm × 23 mm × 7 mm with a mass of ~ 3.78 g. This fully implantable batteryless stimulator provided a simplified and efficient method to carry out chronic experiments in untethered animals for medical electro-neurological research.

Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalographic (EEG) signals based on wavelet packet decomposition (WPD) is used. The coefficients mean of wavelet packet decomposition and wavelet packet energy of special sub-bands are employed as the original features. The Fisher discriminant analysis (FDA) is used to measure the separabilities of those features. The features which had a higher separability will be considered as effective ones and then the final feature vector are formed. A feature vector is obtained by combining the selected features from six channels. Then, the features are classified by using the k-nearest neighbor (k-NN) algorithm. We obtained significant improvement for the speed and accuracy of the classification for data set Ia, which is a typical representative of one kind of BCI competition 2003 data. The classification results have proved the effectiveness of the proposed method.

Recent studies have shown that motor recovery following spinal cord injury (SCI) is task-specific. However, most consequential conclusions about locomotor functional recovery from SCI have been derived from quadrupedal locomotion paradigms. In this study, two monkeys were trained to perform a bipedal walking task, mimicking human walking, before and after T8 spinal cord hemisection. Importantly, there is no pharmacological therapy with nerve growth factor for monkeys after SCI; thus, in this study, the changes that occurred in the brain were spontaneous. The impairment of locomotion on the ipsilateral side was more severe than that on the contralateral side. We used information theory to analyze single-cell activity from the left primary motor cortex (M1), and results show that neuronal populations in the unilateral primary motor cortex gradually conveyed more information about the bilateral hindlimb muscle activities during the training of bipedal walking after SCI. We further demonstrated that, after SCI, progressively expanded information from the neuronal population reconstructed more accurate control of muscle activity. These results suggest that, after SCI, the unilateral primary motor cortex could gradually regain control of bilateral coordination and motor recovery and in turn enhance the performance of brain–machine interfaces.

Providing a neural prosthesis with sensory feedback is essential for refined manipulation. The prosthesis based on a Schunk Dexterous Hand for grasping is supplemented by electrical stimulation to generate time-variant stimulation patterns to offer sensory feedback to amputees. The contact force measured by tactile array sensors on the prosthesis is converted into corresponding patterns used for electrical stimulation of peripheral sensory nerves. The sensory feedback experiment and the stimulation pattern generation experiment were conducted individually, verifying that sensory information can be reliably measured and time-variant stimulation can be realized. Based on these preliminary experiments and simulation results from the novel prosthesis system, we will conduct integrated in vitro experiments to develop neutrally controlled intelligent prostheses.