Sunday, March 9, 2014

Amy Orsborn: March 12th

Title: Exploring decoder and neural adaptation in brain-machine interfaces

Brain-machine interfaces (BMIs) show great promise for restoring motor function to patients with motor disabilities, but significant improvements in performance are needed before they will be clinically viable. Moreover, BMIs must ultimately provide long-term performance that can be used in a variety of settings. One key challenge is to improve performance such that it can be maintained for long-term use in the varied activities of daily life. BMI creates an artificial, closed-loop control system, where the subject actively contributes to performance by volitional modulation of neural activity. In this talk, I will discuss experimental work in non-human primates exploring closed-loop design of BMI, which exploit the closed-loop and adaptive properties of BMI to improve performance and reliability. I will present a closed-loop decoder adaptation (CLDA) algorithm that can rapidly and reliably improve performance regardless of the initial decoding algorithm, which may be particularly useful for clinical applications with paralyzed patients. I will then show that this CLDA can be combined with neural adaptation to achieve and maintain skillful BMI performance across different tasks. Analyses of these data also suggests that brain-decoder interactions might be useful for shaping BMI performance. Finally, I will discuss emerging work exploring the selection of the neural signals for control and how it might influence closed-loop performance. 

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