A brain machine interface control algorithm designed from a feedback control perspective

We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alte...

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Bibliographic Details
Main Authors: Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Ryu, S. I., Shenoy, K. V.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Decoding
Kalman filters
Kinematics
Prosthetics
Technological innovation
Uncertainty
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Summary:We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the methods by which these algorithms are trained. In online neural control experiments recording from a 96-electrode array implanted in M1 of a macaque monkey, the ReFIT-KF control algorithm demonstrates large performance improvements over the current state of the art velocity Kalman filter, reducing target acquisition time by a factor of two, while maintaining a 500 ms hold period, thereby increasing the clinical viability of BMI systems.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2012.6346180