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Improved decoding of limb-state feedback from natural sensors
- Source :
- 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
- Publication Year :
- 2009
- Publisher :
- IEEE, 2009.
-
Abstract
- Limb state feedback is of great importance for achieving stable and adaptive control of FES neuroprostheses. A natural way to determine limb state is to measure and decode the activity of primary afferent neurons in the limb. The feasibility of doing so has been demonstrated by [1] and [2]. Despite positive results, some drawbacks in these works are associated with the application of reverse regression techniques for decoding the afferent neuronal signals. Decoding methods that are based on direct regression are now favored over reverse regression for decoding neural responses in higher regions in the central nervous system [3]. In this paper, we apply a direct regression approach to decode the movement of the hind limb of a cat from a population of primary afferent neurons. We show that this approach is more principled, more efficient, and more generalizable than reverse regression.
- Subjects :
- Adaptive control
Neuroprosthetics
Nerve net
Computer science
Transducers
Central nervous system
Population
Article
Afferent Neurons
Feedback
Afferent
medicine
Animals
Functional electrical stimulation
education
Neurons
Electronic Data Processing
education.field_of_study
Models, Statistical
Signal Processing, Computer-Assisted
Robotics
Biomechanical Phenomena
Hindlimb
Microelectrode
medicine.anatomical_structure
Cats
Regression Analysis
Nerve Net
Neurofeedback
Microelectrodes
Neuroscience
Algorithms
Decoding methods
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Accession number :
- edsair.doi.dedup.....6f738f22f9dcb62b30c176ec5ffd61be