A low cost vlsi architecture for spike sorting based on feature extraction with peak search

Yuan Jyun Chang, Wen Jyi Hwang*, Chih Chang Chen


研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)


The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

出版狀態已發佈 - 2016 12月

ASJC Scopus subject areas

  • 分析化學
  • 資訊系統
  • 原子與分子物理與光學
  • 生物化學
  • 儀器
  • 電氣與電子工程


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