Grand Challenge on Neural Decoding for Motor Control of non-Human Primates

Biyan Zhou; Pao Sheng Vincent Sun; Jason Yik; Charlotte Frenkel; Vijay Janapa Reddi; Arindam Basu

doi:10.1109/BioCAS61083.2024.10798373

Grand Challenge on Neural Decoding for Motor Control of non-Human Primates

Biyan Zhou^*, Pao Sheng Vincent Sun, Jason Yik, Charlotte Frenkel, Vijay Janapa Reddi, Arindam Basu

^*Corresponding author for this work

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Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.

Original language	English
Title of host publication	2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9798350354959
DOIs	https://doi.org/10.1109/BioCAS61083.2024.10798373
Publication status	Published - 2024
Event	2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024 - Xi�an, China Duration: 24 Oct 2024 → 26 Oct 2024

Publication series

Name	2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024

Conference

Conference	2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024
Country/Territory	China
City	Xi�an
Period	24/10/24 → 26/10/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

benchmarking
Grand challenge
implantable BMIs
machine learning
neuromorphic systems

Access to Document

10.1109/BioCAS61083.2024.10798373

Cite this

@inproceedings{d19566587de64b898c0819a9d732e4fd,

title = "Grand Challenge on Neural Decoding for Motor Control of non-Human Primates",

abstract = "To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.",

keywords = "benchmarking, Grand challenge, implantable BMIs, machine learning, neuromorphic systems",

author = "Biyan Zhou and Sun, {Pao Sheng Vincent} and Jason Yik and Charlotte Frenkel and Reddi, {Vijay Janapa} and Arindam Basu",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024 ; Conference date: 24-10-2024 Through 26-10-2024",

year = "2024",

doi = "10.1109/BioCAS61083.2024.10798373",

language = "English",

series = "2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024",

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booktitle = "2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024",

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}

Zhou, B, Sun, PSV, Yik, J, Frenkel, C, Reddi, VJ & Basu, A 2024, Grand Challenge on Neural Decoding for Motor Control of non-Human Primates. in 2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024. 2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024, IEEE, 2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024, Xi�an, China, 24/10/24. https://doi.org/10.1109/BioCAS61083.2024.10798373

Grand Challenge on Neural Decoding for Motor Control of non-Human Primates. / Zhou, Biyan; Sun, Pao Sheng Vincent; Yik, Jason et al.
2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024. IEEE, 2024. (2024 IEEE Biomedical Circuits and Systems Conference, BioCAS 2024).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Reddi, Vijay Janapa

AU - Basu, Arindam

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.

AB - To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.

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Y2 - 24 October 2024 through 26 October 2024

ER -

Grand Challenge on Neural Decoding for Motor Control of non-Human Primates

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Keywords

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