An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.

ABSTRACT: Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to bet

SUBMITTER: Li S 

PROVIDER: S-EPMC5177654 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature