Han, Sungmin Chu, Jun-Uk Park, Jong Woong Youn, Inchan 2024-01-19T21:01:03Z 2024-01-19T21:01:03Z 2021-09-02 2019-02 0929-5313 https://pubs.kist.re.kr/handle/201004/120405 Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems. English SPRINGER ALGORITHMS ENSEMBLES FEEDBACK SYSTEMS Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings Article 10.1007/s10827-018-0686-8 1 JOURNAL OF COMPUTATIONAL NEUROSCIENCE, v.46, no.1, pp.77 - 90 JOURNAL OF COMPUTATIONAL NEUROSCIENCE 46 1 77 90 scie scopus 000463591300006 2-s2.0-85046893171 Mathematical & Computational Biology Neurosciences Mathematical & Computational Biology Neurosciences & Neurology Article ALGORITHMS ENSEMBLES FEEDBACK SYSTEMS Linear feature projection Projection pursuit Negentropy maximization Proprioceptive afferent Kalman filter