A programmable implementation of neural signal processing on a smartdust for brain-computer interfaces

  • Authors:

  • Yuwen Sun;Shimeng Huang;Joseph Oresko;John Krais;Allen C. Cheng

  • Affiliations:

  • University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA;University of Pittsburgh, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
  • Year:
  • 2009

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Abstract

Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or ASIC designs, which lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance trade-off analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.