A 41 μW real-time adaptive neural spike classifier

A. Zjajo, R. van Leuken

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
25 Downloads (Pure)

Abstract

Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike detection, and a boosted cascade, multiclass kernel support vector machine classification. The power-efficient classification is obtained with a combination of the algorithm and circuit techniques. The classifier implemented in a 65 nm CMOS technology consumes less than 41 μW of power, and occupy an area of 2.64 mm2.
Original languageEnglish
Title of host publication2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages489-492
Number of pages4
ISBN (Electronic)978-1-5090-2455-1
DOIs
Publication statusPublished - 21 Apr 2016
Event2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: 24 Feb 201627 Feb 2016
Conference number: 3rd
http://bhi.embs.org/2016/

Conference

Conference2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Abbreviated titleBHI
CountryUnited States
CityLas Vegas
Period24/02/1627/02/16
Internet address

Keywords

  • Kernel
  • Feature extraction
  • Registers
  • Support vector machine classification
  • Sorting
  • Training

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