- Adaptive decoding for brain-machine interfaces through Bayesian parameter updatesZheng Li
Department of Neurobiology and Center for Neuroengineering, Duke University, Durham, NC 27710, U S A
Neural Comput 23:3162-204. 2011..These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable...
- Future developments in brain-machine interface researchMikhail A Lebedev
Duke University, Durham, NC, USA
Clinics (Sao Paulo) 66:25-32. 2011....
- Unscented Kalman filter for brain-machine interfacesZheng Li
Department of Computer Science, Duke University, Durham, North Carolina, United States of America
PLoS ONE 4:e6243. 2009..The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation...