Automated stimulus-response mapping of high-electrode-count neural implants

Andrew M. Wilder, Scott D. Hiatt, Brett R. Dowden, Nicholas A.T. Brown, Richard A. Normann, Gregory A. Clark

Research output: Contribution to journalReview article

14 Citations (Scopus)

Abstract

Over the past decade, research in the field of functional electrical stimulation (FES) has led to a new generation of high-electrode-count (HEC) devices that offer increasingly selective access to neural populations. Incorporation of these devices into research and clinical applications, however, has been hampered by the lack of hardware and software platforms capable of taking full advantage of them. In this paper, we present the first generation of a closed-loop FES platform built specifically for HEC neural interface devices. The platform was designed to support a wide range of stimulus-response mapping and feedback-based control routines. It includes a central control module, a 1100-channel stimulator, an array of biometric devices, and a 160-channel data recording module. To demonstrate the unique capabilities of this platform, two automated software routines for mapping stimulus-response properties of implanted HEC devices were implemented and tested. The first routine determines stimulation levels that produce perithreshold muscle activity, and the second generates recruitment curves (as measured by peak impulse response). Both routines were tested on 100-electrode Utah Slanted Electrode Arrays (USEAs) implanted in cat hindlimb nerves using joint torque or emg as muscle output metric. Mean time to map perithreshold stimulus level was 16.4 s for electrodes that evoked responses (n = 3200), and 3.6 s for electrodes that did not evoke responses (n = 1800). Mean time to locate recruitment curve asymptote for an electrode (n = 155) was 9.6 s , and each point in the recruitment curve required 0.87 s. These results demonstrate the utility of our FES platform by showing that it can be used to completely automate a typically time- and effort-intensive procedure associated with using HEC devices.

Original languageEnglish
Article number5196809
Pages (from-to)504-511
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume17
Issue number5
DOIs
Publication statusPublished - 1 Oct 2009
Externally publishedYes

Fingerprint

Electrodes
Equipment and Supplies
Electric Stimulation
Implanted Electrodes
Software
Muscle
Muscles
Torque
Hindlimb
Research
Data recording
Biometrics
Cats
Impulse response
Joints
Feedback
Hardware
Population

Cite this

Wilder, Andrew M. ; Hiatt, Scott D. ; Dowden, Brett R. ; Brown, Nicholas A.T. ; Normann, Richard A. ; Clark, Gregory A. / Automated stimulus-response mapping of high-electrode-count neural implants. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2009 ; Vol. 17, No. 5. pp. 504-511.
@article{6ccd77a9ac594b62b5c3a5183d266162,
title = "Automated stimulus-response mapping of high-electrode-count neural implants",
abstract = "Over the past decade, research in the field of functional electrical stimulation (FES) has led to a new generation of high-electrode-count (HEC) devices that offer increasingly selective access to neural populations. Incorporation of these devices into research and clinical applications, however, has been hampered by the lack of hardware and software platforms capable of taking full advantage of them. In this paper, we present the first generation of a closed-loop FES platform built specifically for HEC neural interface devices. The platform was designed to support a wide range of stimulus-response mapping and feedback-based control routines. It includes a central control module, a 1100-channel stimulator, an array of biometric devices, and a 160-channel data recording module. To demonstrate the unique capabilities of this platform, two automated software routines for mapping stimulus-response properties of implanted HEC devices were implemented and tested. The first routine determines stimulation levels that produce perithreshold muscle activity, and the second generates recruitment curves (as measured by peak impulse response). Both routines were tested on 100-electrode Utah Slanted Electrode Arrays (USEAs) implanted in cat hindlimb nerves using joint torque or emg as muscle output metric. Mean time to map perithreshold stimulus level was 16.4 s for electrodes that evoked responses (n = 3200), and 3.6 s for electrodes that did not evoke responses (n = 1800). Mean time to locate recruitment curve asymptote for an electrode (n = 155) was 9.6 s , and each point in the recruitment curve required 0.87 s. These results demonstrate the utility of our FES platform by showing that it can be used to completely automate a typically time- and effort-intensive procedure associated with using HEC devices.",
keywords = "Electrode mapping, Functional electrical stimulation (FES), High-electrode-count (HEC) microelectrode device, Motor prosthesis, Recruitment curve, Utah Slanted Electrode Array (USEA)",
author = "Wilder, {Andrew M.} and Hiatt, {Scott D.} and Dowden, {Brett R.} and Brown, {Nicholas A.T.} and Normann, {Richard A.} and Clark, {Gregory A.}",
year = "2009",
month = "10",
day = "1",
doi = "10.1109/TNSRE.2009.2029494",
language = "English",
volume = "17",
pages = "504--511",
journal = "IEEE Transactions on Rehabilitation Engineering",
issn = "1534-4320",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
number = "5",

}

Automated stimulus-response mapping of high-electrode-count neural implants. / Wilder, Andrew M.; Hiatt, Scott D.; Dowden, Brett R.; Brown, Nicholas A.T.; Normann, Richard A.; Clark, Gregory A.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 17, No. 5, 5196809, 01.10.2009, p. 504-511.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Automated stimulus-response mapping of high-electrode-count neural implants

AU - Wilder, Andrew M.

AU - Hiatt, Scott D.

AU - Dowden, Brett R.

AU - Brown, Nicholas A.T.

AU - Normann, Richard A.

AU - Clark, Gregory A.

PY - 2009/10/1

Y1 - 2009/10/1

N2 - Over the past decade, research in the field of functional electrical stimulation (FES) has led to a new generation of high-electrode-count (HEC) devices that offer increasingly selective access to neural populations. Incorporation of these devices into research and clinical applications, however, has been hampered by the lack of hardware and software platforms capable of taking full advantage of them. In this paper, we present the first generation of a closed-loop FES platform built specifically for HEC neural interface devices. The platform was designed to support a wide range of stimulus-response mapping and feedback-based control routines. It includes a central control module, a 1100-channel stimulator, an array of biometric devices, and a 160-channel data recording module. To demonstrate the unique capabilities of this platform, two automated software routines for mapping stimulus-response properties of implanted HEC devices were implemented and tested. The first routine determines stimulation levels that produce perithreshold muscle activity, and the second generates recruitment curves (as measured by peak impulse response). Both routines were tested on 100-electrode Utah Slanted Electrode Arrays (USEAs) implanted in cat hindlimb nerves using joint torque or emg as muscle output metric. Mean time to map perithreshold stimulus level was 16.4 s for electrodes that evoked responses (n = 3200), and 3.6 s for electrodes that did not evoke responses (n = 1800). Mean time to locate recruitment curve asymptote for an electrode (n = 155) was 9.6 s , and each point in the recruitment curve required 0.87 s. These results demonstrate the utility of our FES platform by showing that it can be used to completely automate a typically time- and effort-intensive procedure associated with using HEC devices.

AB - Over the past decade, research in the field of functional electrical stimulation (FES) has led to a new generation of high-electrode-count (HEC) devices that offer increasingly selective access to neural populations. Incorporation of these devices into research and clinical applications, however, has been hampered by the lack of hardware and software platforms capable of taking full advantage of them. In this paper, we present the first generation of a closed-loop FES platform built specifically for HEC neural interface devices. The platform was designed to support a wide range of stimulus-response mapping and feedback-based control routines. It includes a central control module, a 1100-channel stimulator, an array of biometric devices, and a 160-channel data recording module. To demonstrate the unique capabilities of this platform, two automated software routines for mapping stimulus-response properties of implanted HEC devices were implemented and tested. The first routine determines stimulation levels that produce perithreshold muscle activity, and the second generates recruitment curves (as measured by peak impulse response). Both routines were tested on 100-electrode Utah Slanted Electrode Arrays (USEAs) implanted in cat hindlimb nerves using joint torque or emg as muscle output metric. Mean time to map perithreshold stimulus level was 16.4 s for electrodes that evoked responses (n = 3200), and 3.6 s for electrodes that did not evoke responses (n = 1800). Mean time to locate recruitment curve asymptote for an electrode (n = 155) was 9.6 s , and each point in the recruitment curve required 0.87 s. These results demonstrate the utility of our FES platform by showing that it can be used to completely automate a typically time- and effort-intensive procedure associated with using HEC devices.

KW - Electrode mapping

KW - Functional electrical stimulation (FES)

KW - High-electrode-count (HEC) microelectrode device

KW - Motor prosthesis

KW - Recruitment curve

KW - Utah Slanted Electrode Array (USEA)

UR - http://www.scopus.com/inward/record.url?scp=70449482376&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2009.2029494

DO - 10.1109/TNSRE.2009.2029494

M3 - Review article

VL - 17

SP - 504

EP - 511

JO - IEEE Transactions on Rehabilitation Engineering

JF - IEEE Transactions on Rehabilitation Engineering

SN - 1534-4320

IS - 5

M1 - 5196809

ER -