Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Shoeb Shaikh; Rosa So; Tafadzwa Sibindi; Camilo Libedinsky; Arindam Basu

doi:10.1109/NER.2019.8717122

Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Shoeb Shaikh
, Rosa So
, Tafadzwa Sibindi
, Camilo Libedinsky
, Arindam Basu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

Abstract

This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of ≈ 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of ≈ 85% of those obtained by hand-controlled joystick. These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.

Original language	English
Title of host publication	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Publisher	IEEE Computer Society
Pages	670-673
Volume	2019-March
ISBN (Print)	9781538679210
DOIs	https://doi.org/10.1109/NER.2019.8717122
Publication status	Published - 16 May 2019
Externally published	Yes
Event	9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States Duration: 20 Mar 2019 → 23 Mar 2019 http://neuro.embs.org/2019

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2019-March
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Place	United States
City	San Francisco
Period	20/03/19 → 23/03/19
Internet address	http://neuro.embs.org/2019

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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Shaikh, S., So, R., Sibindi, T., Libedinsky, C., & Basu, A. (2019). Real-time Closed Loop Neural Decoding on a Neuromorphic chip. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (Vol. 2019-March, pp. 670-673). Article 8717122 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717122

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title = "Real-time Closed Loop Neural Decoding on a Neuromorphic chip",

abstract = "This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of ≈ 96\% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of ≈ 85\% of those obtained by hand-controlled joystick. These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.",

author = "Shoeb Shaikh and Rosa So and Tafadzwa Sibindi and Camilo Libedinsky and Arindam Basu",

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Shaikh, S, So, R, Sibindi, T, Libedinsky, C & Basu, A 2019, Real-time Closed Loop Neural Decoding on a Neuromorphic chip. in 9th International IEEE EMBS Conference on Neural Engineering, NER 2019. vol. 2019-March, 8717122, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2019-March, IEEE Computer Society, pp. 670-673, 9th International IEEE EMBS Conference on Neural Engineering, NER 2019, San Francisco, United States, 20/03/19. https://doi.org/10.1109/NER.2019.8717122

Real-time Closed Loop Neural Decoding on a Neuromorphic chip. / Shaikh, Shoeb; So, Rosa; Sibindi, Tafadzwa et al.
9th International IEEE EMBS Conference on Neural Engineering, NER 2019. Vol. 2019-March IEEE Computer Society, 2019. p. 670-673 8717122 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - So, Rosa

AU - Sibindi, Tafadzwa

AU - Libedinsky, Camilo

AU - Basu, Arindam

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of ≈ 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of ≈ 85% of those obtained by hand-controlled joystick. These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.

AB - This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of ≈ 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of ≈ 85% of those obtained by hand-controlled joystick. These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.

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Real-time Closed Loop Neural Decoding on a Neuromorphic chip

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