An Active Learning Algorithm for Control of Epidural Electrostimulation

Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm’s performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions’ results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

Optimizing sensory stimulation in humans after spinal cord injury

Sensory stimulation has shown promise in improving human walking after spinal cord injury (SCI). Previous studies have demonstrated some improvement with open-loop, non-individualized sensory stimulation, but after SCI, there are many unique, individual changes in sensorimotor processing. These changes make a priori identification of the best sensory stimulation pattern difficult for any given individual. Real-time optimization provides a solution to this individuality problem, through optimizing sensory stimulation parameters for a given subject in on-line (in real-time). In this research, I developed an approach to optimize sensory stimulation to maximally assist human walking after incomplete SCI. To do so, I had to develop and validate a novel optimization algorithm for globally-optimizing noisy, time-variant, black-box systems, while maximizing the information gained from each test (experiment). I optimized sensory stimulation across a range of SCI subjects, across multiple sensory stimulation sites, and with different stimulation parameterizations. In all subjects and stimulation sites, the optimal stimulation protocol produced better walking (i.e. less external force assistance was required) than three alternative stimulation protocols: an industry-standard stimulation protocol, a no-stimulation protocol, and a random-stimulation protocol. The optimization approach minimized the total force required from an assistive orthosis, and post-hoc analysis of the optimization sessions produced a better understanding of how stimulation parameters affected specific gait features (e.g. hip forces during swing). Transcutaneous spinal cord stimulation (TSCS) frequency had divergent effects on the stance and swing phases – high frequencies tended to assist with swing, but low frequencies tended to assist with stance. For the two peripheral nerve stimulation sites (posterior tibial and common peroneal nerves), the optimal gait-phase for stimulation was generally after mid-stance and before early swing. There was some variability within this time-range depending on the specific feature under study. Experimental history (i.e. time spent walking/time spent being stimulated) proved to be as important a predictor as any of the stimulation parameters.Ph.D

An Active Learning Algorithm for Control of Epidural Electrostimulation

Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions' results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy