A 32-Channel MCU-Based Feature Extraction and Classification for Scalable on-Node Spike Sorting

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Title: A 32-Channel MCU-Based Feature Extraction and Classification for Scalable on-Node Spike Sorting
Author(s): Barsakcioglu, DY
Constandinou, TG
Item Type: Conference Paper
Abstract: This paper describes a new hardware-efficient method and implementation for neural spike sorting based on selection of a channel-specific near-optimal subset of fea- tures given a larger predefined set. For each channel, real- time classification is achieved using a simple decision matrix that considers the features that provide the highest separability determined through off-line training. A 32-channel system for on- line feature extraction and classification has been implemented in an ARM Cortex-M0+ processor. Measured results of the hardware platform consumes 268 W per channel during spike sorting (includes detection). The proposed method provides at least x10 reduction in computational requirements compared to literature, while achieving an average classification error of less than 10% across wide range of datasets and noise levels.
Publication Date: 23-May-2016
Date of Acceptance: 10-Jan-2016
URI: http://hdl.handle.net/10044/1/30123
DOI: https://dx.doi.org/10.1109/ISCAS.2016.7527489
Publisher: IEEE
Start Page: 1310
End Page: 1313
Volume: 1
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/I000569/1
EP/K015060/1
RES/0560/7386 & EFXD12018
EP/M020975/1
EESA_P59880
Conference Name: IEEE International Symposium on Circuits and Systems (ISCAS)
Publication Status: Published
Start Date: 2016-05-22
Finish Date: 2016-05-25
Conference Place: Montreal, Canada
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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