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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.(More)
Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the(More)
OBJECTIVE Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous,(More)
Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation.(More)
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(More)
Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial(More)
Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every(More)
Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we(More)
Recent progress in brain-machine interfaces (BMIs) has shown tremendous improvements in task complexity and degree of control. In particular, closed-loop decoder adaptation (CLDA) has emerged as an effective paradigm for both improving and maintaining the performance of BMI systems. Here, we demonstrate the first reported use of a CLDA algorithm to rapidly(More)