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Closed-loop decoder adaptation (CLDA) shows great promise to improve closed-loop brain–machine interface (BMI) performance. Developing adaptation algorithms capable of rapidly improving performance, independent of initial performance, may be crucial for clinical applications where patients have limited movement and sensory abilities due to motor deficits. Given the subject-decoder interactions inherent...
Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that...
Brain-Machine Interface (BMI) decoding algorithms are often trained offline, but this paradigm ignores both the non-stationarity of neural signals and the feedback that exists in online, closed-loop control. To address these problems, we have developed an Adaptive Kalman Filter (AKF), a Kalman filter variant that adaptively updates its model parameters during training. For a Kalman filter decoder,...
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