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

Published: 19 August 2009 Publication History
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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.

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

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          cover image ACM Conferences
          ISLPED '09: Proceedings of the 2009 ACM/IEEE international symposium on Low power electronics and design
          August 2009
          452 pages
          ISBN:9781605586847
          DOI:10.1145/1594233

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          Association for Computing Machinery

          New York, NY, United States

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          Published: 19 August 2009

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          Author Tags

          1. DVFS
          2. brain-computer interface
          3. brain-implantable computing
          4. smartdust
          5. tinyOS

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          ISLPED '09 Paper Acceptance Rate 72 of 208 submissions, 35%;
          Overall Acceptance Rate 398 of 1,159 submissions, 34%

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