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To date, the scope of brain-machine interfaces (BMIs) has largely been to restore lost function to people with paralysis stemming from conditions such as neurodegenerative disease and spinal cord injury. These systems interface with the brain using neurosurgically implanted electrodes, measure the voltage of individual and groups of neurons, and translate these measurements via a decoding algorithm to control an end effector such as a computer cursor. I will discuss work performed in preclinical rhesus models that led to the highest performing communication BMI demonstrated to date, as well as recent results of an ongoing clinical trial where these preclinical algorithmic innovations have been successfully translated to a human participant, again yielding the highest communication rates of any known clinical BMI.
The example of prosthetics is just one important application leveraging intracortical BMIs as a platform for accurately assessing and acting on the neural state. However, these measurements could play a crucial role in the diagnosis and management of a wide range of brain-related diseases and disorders. Just as EEG recordings help localize seizures both temporally and spatially, and MRI imaging provides morphological and gross functional evaluations of the brain, BMI systems may reveal previously unrecognized disease-specific adulterations in the neural state. Not only could this aid in forming better prognoses, but may also lead to interventions to prevent or alleviate undesirable symptoms and improve rehabilitation. In this manner, the utility of BMIs could extend beyond communication or motor prosthetics to become an indispensable clinical tool in the treatment of brain disorders. I will discuss the emerging potential and key initial steps of this new class of medical system.
Paul is a physician-engineer starting as an Assistant Professor in the Department of Bioengineering, Neurosurgery, and (by courtesy) Electrical Engineering in the Spring of 2017. He completed his undergraduate training at UCLA in 2006. He earned his MS and PhD in Bioengineering at Stanford in 2011 and 2012, respectively, with Krishna Shenoy in the Neural Prosthetics Systems Laboratory and completed his MD from Stanford in 2014. His postdoctoral work was with Jaimie Henderson and Krishna Shenoy in the Neural Prosthetics Translational Laboratory. His research for the past decade has been in the field of intracortical neural prostheses via brain-machine interfaces in both non-human primate animal models and in human clinical trials. His work led to the highest performing communication brain-machine interfaces to date. His research focus moving forward will be to expand the application of brain-machine interfaces to other neurological diseases such as stroke and epilepsy via novel non-human primate animal models and human clinical trials.
