Neural interfaces are engineered devices that directly link the nervous system to the outside world. Our lab explores neural interfaces that operate in two different directions: 1) use the body’s internal neural activity to influence the outside world, and 2) use signals from the outside world to influence the body’s nervous system. For an example of (1), we develop computer algorithms that try to predict a person’s intentions from their brain signals, and use those intention estimations to operate a computer cursor or prosthetic limb. For an example of (2), we apply electrical current to the nerves of the urinary tract based on behavioral cues to improve urinary bladder function. The details of some of our projects are described below:

        Brain-Computer Interface Design and Validation

        One of the challenges limiting the effectiveness of current brain-computer interfaces is the difficulty in designing an algorithm that can both decode the user’s intent from abstract neural signals and constantly adapt to the user’s changing strategies for operating the device. Our work uses a novel paradigm, based on artificial neural networks and computational neuroscience, that allows us to test many such algorithms in closed-loop with many users to determine which ones improve user performance the most, and why. Our studies have yielded insights into the limitations of focusing algorithm design on task error, and the potential problems associated with computer algorithms that learn faster than the human user.

        Neural Control of the Lower Urinary Tract

        All clinical strategies to treat lower urinary tract dysfunction, be they neuromodulation or pharmacotherapy, rely on a basic theory of how the system functions. By probing the neurophysiology of the urinary tract with animal models, our lab is refining our theory of urinary tract control and function, which will lead to new therapies and improvements of existing treatments. We have discovered a number of features about how sensory information is represented and processed in the lower urinary tract, and how this could affect reflex control of the bladder and urethra.

        Targeted Electrical Stimulation of the Lower Urinary Tract

        As we age or are afflicted with neurological injury, our bladders no longer function effectively, which prevents hundreds of thousands of people from properly emptying their bladder. We are using electrophysiology and behavioral studies in animal models to examine why the lower urinary tract loses its functionality as we age or suffer a spinal cord injury. We are delivering electrical stimulation to the nerves that control the lower urinary tract to restore some lost bladder functionality. These novel stimulation strategies span different stimulation sites, such as the urethra and spinal cord, and different stimulation modalities, such as closed-loop stimulation triggered off bladder events or low-amplitude stimulation designed to boost neural sensitivity.

        Co-Adaptation of Human and Artificial Learners

        Increasingly often people have to learn how to perform complex tasks with the help of technology that is also adapting to the human user. We study how this dual-learning environment affects user control strategies and proficiency and how we can better design artificial systems to optimize human motor learning skill. Our results have shown that simply changing visual cues, even if they are irrelevant to the task, can influence how people learn. This finding is especially important for brain-computer interface development because users must learn to operate a complex new system, and it is not known what feedback is most helpful for them during learning.