Microwatt End-to-End Digital Neural Signal Processing Systems for Motor Intention Decoding

Zhewei Jiang1,a, Chisung Bae2, Joonseong Kang2, Sang Joon Kim2 and Mingoo Seok1,b
1Department of Electrical ngineering, Columbia University, New York, US.
2Samsung Electronics, South Korea


This paper presents microwatt end-to-end digital signal processing (DSP) systems for deployment-stage real-time upperlimb movement intent decoding. This brain computer interface (BCI) DSP systems feature intercellular spike detection, sorting, and decoding operations for a 96-channel prosthetic implant. We design the algorithms for those operations to achieve minimal computation complexity while matching or advancing the accuracy of state-of-art BCI sorting and movement decoding. Based on those algorithms, we architect the DSP hardware with the focus on hardware reuse and event-driven operation. The VLSI implementation of the proposed systems in a 65-nm high- VTH shows that it can achieve 4.82 mW at the supply voltage of 300mV in the post-layout simulation. The area is 0.16 mm2.

Keywords: Brain machine interface, Prosthesis, Low power VLSI, Kalman filter.

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