Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Biomedical Engineering
First Advisor's Name
Ranu Jung
First Advisor's Committee Title
Committee chair
Second Advisor's Name
James Abbas
Second Advisor's Committee Title
Committee member
Third Advisor's Name
Kenneth Horch
Third Advisor's Committee Title
Committee member
Fourth Advisor's Name
Jacob McPherson
Fourth Advisor's Committee Title
Committee member
Fifth Advisor's Name
Anthony Dick
Fifth Advisor's Committee Title
Committee member
Keywords
Neural interfaces, Electrical stimulation, Neurostimulation, Neuroprosthetics, Sensory feedback, Electroceuticals, Amputation, Prosthetics, Brain-machine interface, Sensory integration
Date of Defense
3-26-2020
Abstract
Electrical stimulation of peripheral afferents has been used to study the sensory neural code and restore lost sensory function after amputation. Recently, implantable neural interfaces have prompted multiple breakthroughs in artificial somatosensory feedback for individuals with amputation, resulting in functional and psychological benefits. Although promising, the invasive nature of these approaches limits wide clinical applications, hindering the development of advanced neuromodulation strategies for intuitive sensory feedback. Transcutaneous (surface) stimulation is a potential non-invasive alternative. However, traditional surface stimulation methods are hampered by inadequate electrode and stimulation parameter fitting, localized discomfort, poor selectivity, and limited percept modulation.
An enhanced surface electrical neurostimulation platform has been developed to address the need for a non-invasive approach capable of selectively eliciting comfortable tactile percepts with a wide range of intensities, that could be used to complete functional tasks. Several strategies were developed and implemented within the platform to achieve these features. First, a novel channel-hopping interleaved pulse scheduling strategy was developed to elicit enhanced tactile percepts while avoiding the discomfort associated with localized charge densities. This strategy was evaluated with able-bodied human subjects and compared with traditional methods. Second, a bio-inspired charge-rate encoding strategy was implemented to enhance the range and gradation of evoked percept intensities. The encoding strategy was evaluated during psychophysical studies with surface stimulation in able-bodied subjects and intrafascicular stimulation in an individual with a transradial amputation. Finally, a series of functional studies with able-bodied subjects evaluated the functional benefits afforded by the enhanced feedback on their ability to determine the size and hardness of virtual objects and perform graded control of virtual grasp force without visual feedback.
Results of these studies suggest that the strategies implemented within the stimulation platform can address the comfort and selectivity limitations of traditional methods and deliver a wide range of graded percepts that can be utilized to complete precise functional tasks. Overall, the use of this platform may eventually allow wide adoption of surface neurostimulation for chronic restoration of sensory function in individuals with amputation and could serve as a testbed for developing more natural neuromodulation strategies before deployment in implantable systems.
Identifier
FIDC008939
ORCID
https://orcid.org/0000-0002-1473-0173
Previously Published In
1. Pena A E, Kuntaegowdanahalli S S, Abbas J J, Patrick J, Horch K W and Jung R 2017 Mechanical fatigue resistance of an implantable branched lead system for a distributed set of longitudinal intrafascicular electrodes Journal of Neural Engineering 14 066014
1. Pena A E, Rincon-Gonzalez L, Abbas J J and Jung R 2019 Effects of vibrotactile feedback and grasp interface compliance on perception and control of a sensorized myoelectric hand PLoS One 14 e0210956
Recommended Citation
Pena, Andres Eduardo, "Enhanced Surface Electrical Neurostimulation (eSENS): A Non-invasive Platform for Peripheral Neuromodulation" (2020). FIU Electronic Theses and Dissertations. 4410.
https://digitalcommons.fiu.edu/etd/4410
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