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Title: An automated system for rapid evaluation of high-density electrode arrays in neural prostheses
Authors: John, Sam
Shivdasani, Mohit
Leuenberger, James
Fallon, James
Shepherd, Robert
Millard, Rodney
Rathbone, Graeme
Williams, Chris
Issue Date: Jun-2011
Publisher: Institute of Physics
Citation: John, S., Shivdasani, M. N., Leuenberger, J., Fallon, J., Shepherd, R., Millard, R., Rathbone, G. D., & Williams, C. E. (2011). An automated system for rapid evaluation of high-density electrode arrays in neural prostheses. Journal of Neural Engineering, 8(3), Article#036011.
???metadata.dc.relation.journal???: Journal of Neural Engineering
Abstract: The success of high density electrode arrays for use in neural prostheses depends on efficient impedance monitoring and fault detection. Conventional methods of impedance testing and fault detection are time consuming and are not always suited for in-vivo assessment of high density electrode arrays. Additionally the ability to evaluate impedances and faults such as open and short circuits both, in-vitro and in-vivo are important to ensure safe and effective stimulation. In the present work we describe an automated system for the rapid evaluation of high density electrode arrays. The system uses a current pulse similar to that used to stimulate neural tissue and measures the voltage across the electrode in order to calculate the impedance. The switching of the system was validated by emulating a high density electrode array using light emitting diodes and a resistor- capacitor network. The system was tested in-vitro and in-vivo using a range of commercially available and in-house developed electrode arrays. The system accurately identified faults on an 84 electrode array in less than 20 seconds and reliably measured impedances up to 110 kn using a 200µA, 250 µs per phase current pulse. This system has direct application for screening high density electrode arrays in both a clinical and experimental setting.
URI: http://repository.bionicsinstitute.org:8080/handle/123456789/10
ISSN: 1741-2560
Appears in Collections:Bionic Vision Research Publications

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