TechRxiv

Benchmarking of Hardware-efficient Real-time Neural Decoding in Brain-computer Interfaces

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Version 2 2023-12-04, 04:03
Version 1 2023-11-13, 17:23
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posted on 2023-12-04, 04:03 authored by Paul Hueber, Guangzhi Tang, Manolis Sifalakis, Hua-Peng Liaw, Aurora Micheli, Nergis Tömen, Yao-Hong LiuYao-Hong Liu

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency.

Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources.

These distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101001448)

History

Email Address of Submitting Author

yao-hong.liu@imec.nl

ORCID of Submitting Author

0000-0002-3256-6741

Submitting Author's Institution

Imec

Submitting Author's Country

  • Netherlands

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