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Methods for estimating neural firing rates, and their application to brain-machine interfaces Cunningham JP; Gilja V; Ryu SI; Shenoy KVNeural Netw 2009[Nov]; 22 (9): 1235-46Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.|*Action Potentials[MESH]|*Signal Processing, Computer-Assisted[MESH]|*User-Computer Interface[MESH]|Algorithms[MESH]|Animals[MESH]|Artificial Limbs[MESH]|Bayes Theorem[MESH]|Biomechanical Phenomena[MESH]|Brain/*physiology[MESH]|Electrodes, Implanted[MESH]|Hand[MESH]|Macaca mulatta[MESH]|Male[MESH]|Microelectrodes[MESH]|Motor Activity/*physiology[MESH]|Neurons/*physiology[MESH]|Normal Distribution[MESH] |
