000175934 001__ 175934
000175934 005__ 20180913061212.0
000175934 0247_ $$2doi$$a10.1088/1741-2560/8/4/046019
000175934 02470 $$2ISI$$a000292962800030
000175934 037__ $$aCONF
000175934 245__ $$aNeuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications
000175934 269__ $$a2011
000175934 260__ $$c2011
000175934 336__ $$aConference Papers
000175934 520__ $$aFunctional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.
000175934 6531_ $$aNormal Locomotion
000175934 6531_ $$aNumerical Data
000175934 6531_ $$aSignals
000175934 6531_ $$aRules
000175934 6531_ $$aHand
000175934 6531_ $$aCat
000175934 6531_ $$aProprioception
000175934 6531_ $$aAfferents
000175934 6531_ $$aIdentification
000175934 6531_ $$aParaplegia
000175934 700__ $$aRigosa, J.$$uScuola Super St Anna Pisa, BioRobot Inst, I-56127 Pisa, Italy
000175934 700__ $$aWeber, D. J.$$uUniv Pittsburgh, Pittsburgh, PA 15213 USA
000175934 700__ $$aProchazka, A.$$uUniv Alberta, Edmonton, AB T6G 2E1, Canada
000175934 700__ $$aStein, R. B.$$uUniv Alberta, Edmonton, AB T6G 2E1, Canada
000175934 700__ $$0246201$$aMicera, S.$$g218366$$uScuola Super St Anna Pisa, BioRobot Inst, I-56127 Pisa, Italy
000175934 7112_ $$a39th Neural Interfaces Conference (NIC2010)$$cLong Beach, CA$$dJun, 2010
000175934 773__ $$j8$$q-$$tJournal Of Neural Engineering
000175934 909C0 $$0252419$$pTNE$$xU12522
000175934 909C0 $$0252517$$pCNP$$xU12599
000175934 909CO $$ooai:infoscience.tind.io:175934$$pconf$$pSTI
000175934 937__ $$aEPFL-CONF-175934
000175934 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000175934 980__ $$aCONF