Evaluation of a minimally invasive endovascular neural interface for decoding motor activity

Ian A. Forsyth, Megan Dunston, Gabriel Lombardi, Gil S. Rind, Stephen Ronayne, Yan T. Wong, Clive N. May, David B. Grayden, Thomas Oxley, Nicholas Opie, Sam E. John

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Endovascular devices like the Stentrode™ provide a minimally invasive approach to brain-machine-interfaces that mitigates safety concerns while maintaining good signal quality. Our research aims to evaluate the feasibility of using a stent-electrode array (Stentrode) to decode movements in sheep. In this study, two sheep were trained to perform an automated forced-choice task designed to elicit left and right head movement following an external stimulus. Cortical, movement-related signals were recorded using a Stentrode placed in the superior sagittal sinus adjacent to the motor cortex. Recorded brain signal was used to train a support vector machine classifier. Our results show that the Stentrode can be used to acquire motor-related brain signals to detect movement of the sheep in a forced-choice task. These results support the validity of using the Stentrode as a minimally invasive brain-machine interface.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering
EditorsMichel Maharbiz, Cynthia Chestek
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages750-753
Number of pages4
ISBN (Electronic)9781538679210
ISBN (Print)9781538679227
DOIs
Publication statusPublished - 2019
EventInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019 - San Francisco, United States of America
Duration: 20 Mar 201923 Mar 2019
Conference number: 9th
https://neuro.embs.org/2019/

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

ConferenceInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019
Abbreviated titleNER 2019
CountryUnited States of America
CitySan Francisco
Period20/03/1923/03/19
Internet address

Cite this

Forsyth, I. A., Dunston, M., Lombardi, G., Rind, G. S., Ronayne, S., Wong, Y. T., ... John, S. E. (2019). Evaluation of a minimally invasive endovascular neural interface for decoding motor activity. In M. Maharbiz, & C. Chestek (Eds.), 9th International IEEE EMBS Conference on Neural Engineering (pp. 750-753). (International IEEE/EMBS Conference on Neural Engineering). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/NER.2019.8717000
Forsyth, Ian A. ; Dunston, Megan ; Lombardi, Gabriel ; Rind, Gil S. ; Ronayne, Stephen ; Wong, Yan T. ; May, Clive N. ; Grayden, David B. ; Oxley, Thomas ; Opie, Nicholas ; John, Sam E. / Evaluation of a minimally invasive endovascular neural interface for decoding motor activity. 9th International IEEE EMBS Conference on Neural Engineering. editor / Michel Maharbiz ; Cynthia Chestek. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 750-753 (International IEEE/EMBS Conference on Neural Engineering).
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title = "Evaluation of a minimally invasive endovascular neural interface for decoding motor activity",
abstract = "Endovascular devices like the Stentrode™ provide a minimally invasive approach to brain-machine-interfaces that mitigates safety concerns while maintaining good signal quality. Our research aims to evaluate the feasibility of using a stent-electrode array (Stentrode) to decode movements in sheep. In this study, two sheep were trained to perform an automated forced-choice task designed to elicit left and right head movement following an external stimulus. Cortical, movement-related signals were recorded using a Stentrode placed in the superior sagittal sinus adjacent to the motor cortex. Recorded brain signal was used to train a support vector machine classifier. Our results show that the Stentrode can be used to acquire motor-related brain signals to detect movement of the sheep in a forced-choice task. These results support the validity of using the Stentrode as a minimally invasive brain-machine interface.",
author = "Forsyth, {Ian A.} and Megan Dunston and Gabriel Lombardi and Rind, {Gil S.} and Stephen Ronayne and Wong, {Yan T.} and May, {Clive N.} and Grayden, {David B.} and Thomas Oxley and Nicholas Opie and John, {Sam E.}",
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Forsyth, IA, Dunston, M, Lombardi, G, Rind, GS, Ronayne, S, Wong, YT, May, CN, Grayden, DB, Oxley, T, Opie, N & John, SE 2019, Evaluation of a minimally invasive endovascular neural interface for decoding motor activity. in M Maharbiz & C Chestek (eds), 9th International IEEE EMBS Conference on Neural Engineering. International IEEE/EMBS Conference on Neural Engineering, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 750-753, International IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019, San Francisco, United States of America, 20/03/19. https://doi.org/10.1109/NER.2019.8717000

Evaluation of a minimally invasive endovascular neural interface for decoding motor activity. / Forsyth, Ian A.; Dunston, Megan; Lombardi, Gabriel; Rind, Gil S.; Ronayne, Stephen; Wong, Yan T.; May, Clive N.; Grayden, David B.; Oxley, Thomas; Opie, Nicholas; John, Sam E.

9th International IEEE EMBS Conference on Neural Engineering. ed. / Michel Maharbiz; Cynthia Chestek. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 750-753 (International IEEE/EMBS Conference on Neural Engineering).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Forsyth, Ian A.

AU - Dunston, Megan

AU - Lombardi, Gabriel

AU - Rind, Gil S.

AU - Ronayne, Stephen

AU - Wong, Yan T.

AU - May, Clive N.

AU - Grayden, David B.

AU - Oxley, Thomas

AU - Opie, Nicholas

AU - John, Sam E.

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AB - Endovascular devices like the Stentrode™ provide a minimally invasive approach to brain-machine-interfaces that mitigates safety concerns while maintaining good signal quality. Our research aims to evaluate the feasibility of using a stent-electrode array (Stentrode) to decode movements in sheep. In this study, two sheep were trained to perform an automated forced-choice task designed to elicit left and right head movement following an external stimulus. Cortical, movement-related signals were recorded using a Stentrode placed in the superior sagittal sinus adjacent to the motor cortex. Recorded brain signal was used to train a support vector machine classifier. Our results show that the Stentrode can be used to acquire motor-related brain signals to detect movement of the sheep in a forced-choice task. These results support the validity of using the Stentrode as a minimally invasive brain-machine interface.

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Forsyth IA, Dunston M, Lombardi G, Rind GS, Ronayne S, Wong YT et al. Evaluation of a minimally invasive endovascular neural interface for decoding motor activity. In Maharbiz M, Chestek C, editors, 9th International IEEE EMBS Conference on Neural Engineering. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 750-753. (International IEEE/EMBS Conference on Neural Engineering). https://doi.org/10.1109/NER.2019.8717000