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

Conference Paper (2024)
Author(s)

Biyan Zhou (City University of Hong Kong)

Pao Sheng Vincent Sun (City University of Hong Kong)

Jason Yik (Harvard School of Engineering and Applied Sciences)

C. Frenkel (TU Delft - Electronic Instrumentation)

Vijay Janapa Reddi (Harvard School of Engineering and Applied Sciences)

Arindam Basu (City University of Hong Kong)

Research Group
Electronic Instrumentation
DOI related publication
https://doi.org/10.1109/BioCAS61083.2024.10798373
More Info
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Publication Year
2024
Language
English
Research Group
Electronic Instrumentation
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. @en
ISBN (electronic)
9798350354959
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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.

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